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Data Scientists
We are pleased to introduce our experienced Data Scientists, professionals with deep expertise in transforming complex data into meaningful insights that support strategic decisions. They have a strong track record of developing predictive models, improving analytical processes, and uncovering patterns that drive efficiency and growth. With their ability to communicate findings clearly and work closely with different teams, they help organizations make smarter decisions, reduce risks, and unlock new opportunities for success.
• Led cross-functional Centers of Excellence (CoEs) across Corporate Finance, Anti-Fraud and Global Purchasing & Supply Chain, delivering insights that generated $13M in cost savings through advanced analytics solutions.
• Developed and deployed a real-time fraud detection system to flag suspicious manual journal entries, reducing manual effort by 80% and enhancing risk identification and operational efficiency.
• Led a high-impact Operational Excellence (OpEx) initiative using the Six Sigma DMAIC methodology to uncover $3M in incorrect incentive payments to GM Mexico dealers and implemented controls to prevent recurrence, strengthening financial governance and compliance.
• Implemented Natural Language Processing (NLP) based text mining applications to optimize the audit plan, driving a 22% increase in department efficiency and reduced annual expenses by $3.4M.
• Developed risk assessment models and continuous business monitoring for warranty spend resulting in 4X higher policy-deviation identification rate. Additionally, the automation led to annual savings of over 1.4 FTEs (~2,880 hours/year) and improved decision-making.
• Proposed and implemented a data governance roadmap that led to generating $2.6M annually through accelerated project delivery, improved data security and enhanced business continuity.
• Leveraged process mining (Power Automate) to streamline material purchasing workflows, enhancing SAP-level data controls. This resulted in an estimated annual impact of $750K through avoidance of pre-commitments and untimely approvals.
• Trained 50+ auditors on digital auditing tools (Gen AI, Power BI and MS Excel), that improved productivity and reduced planning, fieldwork and reporting times by 20%.
• Led cross-functional Centers of Excellence (CoEs) across Corporate Finance, Anti-Fraud and Global Purchasing & Supply Chain, delivering insights that generated $13M in cost savings through advanced analytics solutions.
• Developed and deployed a real-time fraud detection system to flag suspicious manual journal entries, reducing manual effort by 80% and enhancing risk identification and operational efficiency.
• Led a high-impact Operational Excellence (OpEx) initiative using the Six Sigma DMAIC methodology to uncover $3M in incorrect incentive payments to GM Mexico dealers and implemented controls to prevent recurrence, strengthening financial governance and compliance.
• Implemented Natural Language Processing (NLP) based text mining applications to optimize the audit plan, driving a 22% increase in department efficiency and reduced annual expenses by $3.4M.
• Developed risk assessment models and continuous business monitoring for warranty spend resulting in 4X higher policy-deviation identification rate. Additionally, the automation led to annual savings of over 1.4 FTEs (~2,880 hours/year) and improved decision-making.
• Proposed and implemented a data governance roadmap that led to generating $2.6M annually through accelerated project delivery, improved data security and enhanced business continuity.
• Leveraged process mining (Power Automate) to streamline material purchasing workflows, enhancing SAP-level data controls. This resulted in an estimated annual impact of $750K through avoidance of pre-commitments and untimely approvals.
• Trained 50+ auditors on digital auditing tools (Gen AI, Power BI and MS Excel), that improved productivity and reduced planning, fieldwork and reporting times by 20%.
Job description
• Responsible for all the phases of data analytics from conception to completion including business requirements, solution design, data acquisition, analysis, automation and visualization.
• Led multiple analytics training workshops across General Motors to train non-technical teams on data tools including Tableau, SAP, and Alteryx, enabling self-service analytics capabilities across departments.
• Designed and implemented a real-time key performance indicator (KPI) predictive model for the department, reducing manual reporting by over 250 hours monthly, freeing up ~$200K in analyst capacity annually and empowering teams to focus on strategic decision-making.
• Presented at a high-profile STEM event alongside CEO Mary Barra, advocating for data literacy and highlighting its critical role in driving informed organizational decision-making.
Job description
• Analyzed vehicle data collected from vehicle health report, embedded modems and sync modules to gauge the trends and anomalies.
• Developed Tableau dashboards on top of a large-scale view (using SQL) to track the daily module delivery by suppliers (supplier feed), leading to improved visibility resulting in over $200 K annually in avoided operational disruptions and $60 K annually in capacity reallocation.
• Developed POCs, presented to senior management and brought big data search engine – Splunk, to the team.
• Developed a Splunk dashboard to analyze and categorize the Sysalerts (system alerts generated by GIVIS application) by parsing websphere application server (WAS) logs, leading to 36% fewer false positives, 18X faster identification of true issues and better prioritization of alerts.
Job description
• Designed automated data extraction tools for smartphone performance analysis, utilized query optimization techniques for boot-up process improvement, performed root-cause and trend analyses and provided technical reports to leadership on findings.
Recent position
06/01/2021
-
07/11/2025
Skills
Amazon Web Services
Python (Programming Language)
Prompt Engineering
Data Science
Auditing
Governance
Leadership
Visualization
Data Extraction
Performance Analysis
Process Mining
SAP CRM
Planning
Parsing
Solution Design
Automation
Corporate Finance
Fraud Detection
Management
Literacy
Health Risk Assessments
Data Security
SQL (Programming Language)
Business Requirements
Dashboard
Power BI
Advanced Analytics
Purchasing
Analytics
Tableau (Business Intelligence Software)
Research
Finance
Microsoft Excel
Big Data
KNIME
ChatGPT
Business Analytics
Inclusive Leadership
Cloud Computing
Business Intelligence
Databricks
Sync (Unix)
Query Optimization
Operational Excellence
Data Governance
Prioritization
Splunk
Modems
Alteryx
Data Literacy
Define Measure Analyze Improve And Control (DMAIC)
• Data-Driven Solutions: Led development and implementation of analytics strategies across Finance, Compliance, and Retail, achieving:
‣ 36% reduction in accounts receivable via product P&L analysis and credit line optimization.
‣ 18% decrease in reserve accruals through enhanced financial controls and market research.
• Digital Transformation: Spearheaded large-scale digital transformation initiatives, including a new digital sales channel that:
‣ Boosted digital customer acquisition by 23% and online sales by 14%.
‣ Unified cross-functional teams of data scientists, IT developers, risk managers, and finance experts
• Stakeholder Engagement: Aligned data-focused strategies with business goals through weekly sales performance reporting (loans, credit cards, mobile banking), resulting in improved conversion rates at each step and higher engagement among C-level executives, product owners, and development teams.
• Data-Driven Solutions: Led development and implementation of analytics strategies across Finance, Compliance, and Retail, achieving:
‣ 36% reduction in accounts receivable via product P&L analysis and credit line optimization.
‣ 18% decrease in reserve accruals through enhanced financial controls and market research.
• Digital Transformation: Spearheaded large-scale digital transformation initiatives, including a new digital sales channel that:
‣ Boosted digital customer acquisition by 23% and online sales by 14%.
‣ Unified cross-functional teams of data scientists, IT developers, risk managers, and finance experts
• Stakeholder Engagement: Aligned data-focused strategies with business goals through weekly sales performance reporting (loans, credit cards, mobile banking), resulting in improved conversion rates at each step and higher engagement among C-level executives, product owners, and development teams.
Job description
• Advanced Analytics Innovation: Transitioned Farm Management Systems to Python-based solutions (Git-based workflows, cloud
deployment), expediting data-driven product releases by 50%.
• Crop Optimization: Improved hybrid crop testing efficiency by 66% through data-driven soil and climate analyses (Cluster Analysis).
• MVP Launch: Deployed a Farm Management System MVP leveraging ML (WOFOST models), IoT data, and weather forecasts, reducing fertilizer costs by 23% and enhancing yield prediction accuracy to 80%.
• Talent Strategy: Built and led an internship program that attracted top-tier data science/engineering talent.
Job description
• Business Intelligence: Implemented analytics frameworks for postal/retail performance, fueling a 17% branch profitability lift through A/B tested customer headcount forecasts and dynamic branch schedules.
• Executive Engagement: Completely rebuild weekly data-rich reports (200+ KPIs) for board/management, reshaping communication and
accelerating decision-making.
• Reporting Redesign: Enhanced performance storytelling, consolidating KPI groupings to highlight key insights and operational efficiency.
Job description
Lead of Division > Project Director > Executive Director, Private Banking
• Strategic Leadership: Directed data projects (market research, financial modeling) from ideation to cross-functional execution, resulting in consistent revenue and growth milestones.
• Revenue Expansion: Designed and deployed strategic plans, BSC frameworks, and motivational programs to grow Private Banking revenue 10x over five years.
• BI Implementation: Instituted real-time KPI tracking, fraud detection, and product recommendation engines, cutting operational risk by 15% and enhancing compliance.
• Team Leadership: Oversaw multiple teams (Data Scientists, BI analysts), enabling an Agile transformation that improved project outcomes and reduced cycle times.
Job description
• Digital Analytics Function: Advanced from Analyst to Head of Analytics, optimizing digital acquisition with Cost-Per-Acquisition (CPA) models.
• Retention Gains: Cut churn by 48% and increased customer lifetime value via targeted, data-driven retention campaigns.
Job description
Senior Analyst > Associate, CRM / Digital Sales Department
Job description
Skills
Data Science &Analytics: Predictive Modeling (Python [NumPy, pandas, scikit-learn, matplotlib], SQL), Statistical Analysis, A/B Testing, Customer Analytics, Fraud Detection
Big Data & BI: Data Warehousing (SQL, Spark, Hadoop, Vertica, ClickHouse, Postgres), Visualization (Tableau, Qlik View/Sence, Looker Studio), AWS/Azure Cloud Services
Forecasting & Modeling: Advanced KPI Forecasting, Benchmarking, Industry Performance Analysis Business & Leadership: P&L Optimization, Financial Modeling (DCF, ROI, NPV), Agile Project Management, Team Leadership, Strategic Roadmaps
Led a team of 4 high-performing data scientists delivering innovative analytical solutions. Collaborated with business partners to develop predictive models and implement data-driven strategies.
• Developed an in-house Next Best Action targeting product using SQL, Alteryx, Snowflake, and Power BI, resulting in a 20% increase in account openings, $5M in additional annual revenue and improvement in customer engagement.
• Oversaw end-to-end analytics lifecycle, from requirements gathering, problem formulation to data exploration, modelling, validation, and production while collaborating with sales enablement, marketing, product and compliance teams.
• Built and deployed advanced machine learning models (Predicting Customer Churn and Customer Segmentation). Executed strategies to reduce customer churn by 15%, increase deposit balances and deepen customer relationships.
• Optimized marketing campaign performance through data-driven A/B testing, resulting in measurable improvements to key performance indicators, enhancing engagement and actionable recommendations.
• Successfully led and managed agile projects, leveraging tools like JIRA and Azure Boards to streamline workflows, reduce project cycle times by 25%, foster collaboration and increase team productivity.
• Led Snowflake Cloud deployment, cutting costs by 30% and boosting performance and resilience; implemented data governance frameworks, reducing data quality issues by 40% and enhancing efficiency.
Led a team of 4 high-performing data scientists delivering innovative analytical solutions. Collaborated with business partners to develop predictive models and implement data-driven strategies.
• Developed an in-house Next Best Action targeting product using SQL, Alteryx, Snowflake, and Power BI, resulting in a 20% increase in account openings, $5M in additional annual revenue and improvement in customer engagement.
• Oversaw end-to-end analytics lifecycle, from requirements gathering, problem formulation to data exploration, modelling, validation, and production while collaborating with sales enablement, marketing, product and compliance teams.
• Built and deployed advanced machine learning models (Predicting Customer Churn and Customer Segmentation). Executed strategies to reduce customer churn by 15%, increase deposit balances and deepen customer relationships.
• Optimized marketing campaign performance through data-driven A/B testing, resulting in measurable improvements to key performance indicators, enhancing engagement and actionable recommendations.
• Successfully led and managed agile projects, leveraging tools like JIRA and Azure Boards to streamline workflows, reduce project cycle times by 25%, foster collaboration and increase team productivity.
• Led Snowflake Cloud deployment, cutting costs by 30% and boosting performance and resilience; implemented data governance frameworks, reducing data quality issues by 40% and enhancing efficiency.
Job description
• Developed and deployed 4 high-impact, interactive dashboards and reports utilizing SQL, SAS, Teradata, and Tableau to drive strategic decision-making within the Pharmacy Benefit Managers and Specialty business units.
• Aggregated and analysed complex data from 3 diverse sources to create unified data layer, enhancing data accessibility for team of 15 data analysts & consultants, delivering actionable insights to support business growth initiatives.
• Collaborated closely with products and marketing partners to conduct in-depth industry trend analysis, uncover actionable insights, and produce structured reporting to drive strategies.
Job description
• Conducted comprehensive quantitative and qualitative analysis to identify and rectify performance issues within low-performing credit card portfolios, resulting in a 15% improvement in overall card health.
• Developed and implemented strategic analysis, reporting solutions through visually impactful dashboards utilizing SQL, SAS, and SAP BO to visualize performance metrics across 20+ merchant categories for credit and debit cards.
• Streamlined compliance and query resolution processes by reducing turnaround time by 2 days and automating 70% of report generation for debit cards.
• Executed data-driven marketing campaigns and conducted in-depth analysis to drive a 20% increase in debit card sales and enhance overall customer engagement.
• Reporting Solutions and Performance Analytics: Developed and implemented reporGng soluGons and analyGcs to enhance banker performance evaluaGons, collaboraGng with stakeholders to provide data-driven recommendaGons.
• Data Analysis and Interpretation: Recommended yearly goals for bankers based on historical KPIs using predicGve modeling, improving producGvity by 15% and synthesizing insights into acGonable soluGons.
• Innovation and Problem solving: Redefined "New RelaGonship" criteria, boosGng new client acquisiGons by 20% and increasing banker incenGves by $4M in 2023.Introduced a dynamic soluGon for idenGfying 'True' New RelaGonships through liquidity analysis to efficiently separate out clients resulGng from the First Republic Bank merger saving management 16+ hours of manual effort.
• Automation and Streamlining: End-to-end automaGon of 10+ manual reports using Alteryx and Python, saving approximately 800 hours of manual work annually to help create a seamless reporGng experience for our partners and merchants.
• Data-Driven Strategy & Visualization: Created Tableau dashboards to analyze key factors affecGng declines, improving approval rates by 14% and reducing transacGon costs by 10%.
• Merchant Acquisition: Collaborated with cross-funcGonal teams to upsell payment products, acquiring 200+ new merchants with annual revenue exceeding $250M.
• Client Relationship Management: Led Quarterly Business Reviews with top clients, such as Expedia, to support system improvements and business expansion; contributed over $29M in 2022 revenue, with ~$4.7M from Merchant Services.
• Reporting Solutions and Performance Analytics: Developed and implemented reporGng soluGons and analyGcs to enhance banker performance evaluaGons, collaboraGng with stakeholders to provide data-driven recommendaGons.
• Data Analysis and Interpretation: Recommended yearly goals for bankers based on historical KPIs using predicGve modeling, improving producGvity by 15% and synthesizing insights into acGonable soluGons.
• Innovation and Problem solving: Redefined "New RelaGonship" criteria, boosGng new client acquisiGons by 20% and increasing banker incenGves by $4M in 2023.Introduced a dynamic soluGon for idenGfying 'True' New RelaGonships through liquidity analysis to efficiently separate out clients resulGng from the First Republic Bank merger saving management 16+ hours of manual effort.
• Automation and Streamlining: End-to-end automaGon of 10+ manual reports using Alteryx and Python, saving approximately 800 hours of manual work annually to help create a seamless reporGng experience for our partners and merchants.
• Data-Driven Strategy & Visualization: Created Tableau dashboards to analyze key factors affecGng declines, improving approval rates by 14% and reducing transacGon costs by 10%.
• Merchant Acquisition: Collaborated with cross-funcGonal teams to upsell payment products, acquiring 200+ new merchants with annual revenue exceeding $250M.
• Client Relationship Management: Led Quarterly Business Reviews with top clients, such as Expedia, to support system improvements and business expansion; contributed over $29M in 2022 revenue, with ~$4.7M from Merchant Services.
Job description
• Strategic Solutions: Data science consultant for various clients, translaGng key business requirements into acGonable problem statements leveraging predicGve modeling, hypothesis tesGng and staGsGcal inference and effecGvely conveying results to diverse stakeholders through visualizaGons.
• Natural Language Processing: Implemented Tf-idf, sentiment analysis and text summarization to automate insights and visualize market trends of faulty parts using Django eliminaGng manual intervenGon by 95%.
• Machine learning algorithm: Trained the Random Forest and Stochastic Gradient Descent (SGD) Classifiers with accuracy of 90% to extract relevant topics to reduce annual reporGng fines by 60% using customer retenGon dataset of 10M historical records.
• Predictive Modeling and Dashboard Improvement: OpGmized CompeGGve Landscape and ElasGcity dashboards to predict pricing effects and analyze vehicle incenGves, leading to a 14% increase in sales and reducing predicGon errors by 15%.
• Explorative data analysis and feature engineering: Developed stoplight report using random forest model to idenGfy possible factors for claim denial provided acGonable insights and that aided in recovering ~$1M in claims.
• Neural Network Development: Created an adapGve neural network soluGon with TensorFlow and Python libraries (Pandas, NumPy, Scikit-learn) to forecast vehicle part afributes with 87% precision, processing 22M records to enable full automaGon.
Tools & Technologies: Python, SQL, PySpark, Pandas, NumPy, Scikit-learn, TensorFlow, Hugging Face Transformers, GPT-4, LangChain, RAG, FAISS, Weaviate, spaCy, FastAPI, Apache Airflow, Docker, MLflow, AWS (S3, EC2, SageMaker, Lambda), Azure ML Studio, MongoDB
Roles and Responsibilities: Developed machine learning pipelines to automate the classification and summarization of unstructured healthcare documents using Scikit-learn, TensorFlow, and spaCy , streamlining manual review processes. Built predictive models for identifying high-priority patient cases using logistic regression and random forest, supporting clinical risk assessments. Implemented Named Entity Recognition (NER) and part-of-speech tagging with spaCy to extract clinical terms and PHI, enabling structured output for compliance and analytics. Conducted A/B testing to evaluate the impact of M achine Learning driven clinical insights, promoting data-driven decision-making within care teams. Automated ingestion and real-time processing of clinical documents from hospital systems using Kafka and
PySpark , orchestrated through Apache Airflow. Delivered visual insights and model results via Tableau dashboards and exposed API endpoints using FastAPI for seamless integration with internal tools. Led the transition to LLM-powered solutions, designing and deploying a clinical document intelligence system utilizing GPT-4 APIs, LangChain , and Retrieval-Augmented Generation (RAG) for enhanced summarization and decision support. Integrated FAISS and Weaviate vector databases for semantic search, enabling efficient retrieval of contextually relevant medical information. Engineered prompt templates and optimized ( LLM ) Large Language Models responses through advanced prompt engineering strategies tailored for healthcare-specific use cases. Developed a speaker identification and transcription enhancement system using transformer-based models, improving multi-speaker clinical note processing. Containerized LLM workflows using Docker, with deployments across AWS SageMaker and Azure ML Studio to ensure scalable and secure access to AI services. Employed MLflow for model versioning and experiment tracking, supporting reproducibility and collaborative development in multi-cloud environments. Designed and maintained data validation and quality assurance workflows using Pandas, Great Expectations, and custom Python scripts, ensuring integrity across M achine Learning and LLM pipelines. Fine-tuned domain-specific Transformer models (e.g., BioBERT , ClinicalBERT ) using Hugging Face Transformers, enhancing performance on specialized clinical text tasks. Implemented scalable data pipelines on AWS using S3, Glue, and Athena for structured and semi-structured healthcare datasets, enabling efficient querying and model input preparation. Developed custom vectorization and embedding techniques with SentenceTransformers and BERT-as-a-Service to improve semantic understanding in document retrieval tasks. Automated model retraining workflows using Apache Airflow, enabling continuous integration of new data and adaptive learning for deployed ML and LLM models , while fostering a collaborative and solutions-oriented team environment .
Tools & Technologies: Python, SQL, PySpark, Pandas, NumPy, Scikit-learn, TensorFlow, Hugging Face Transformers, GPT-4, LangChain, RAG, FAISS, Weaviate, spaCy, FastAPI, Apache Airflow, Docker, MLflow, AWS (S3, EC2, SageMaker, Lambda), Azure ML Studio, MongoDB
Roles and Responsibilities: Developed machine learning pipelines to automate the classification and summarization of unstructured healthcare documents using Scikit-learn, TensorFlow, and spaCy , streamlining manual review processes. Built predictive models for identifying high-priority patient cases using logistic regression and random forest, supporting clinical risk assessments. Implemented Named Entity Recognition (NER) and part-of-speech tagging with spaCy to extract clinical terms and PHI, enabling structured output for compliance and analytics. Conducted A/B testing to evaluate the impact of M achine Learning driven clinical insights, promoting data-driven decision-making within care teams. Automated ingestion and real-time processing of clinical documents from hospital systems using Kafka and
PySpark , orchestrated through Apache Airflow. Delivered visual insights and model results via Tableau dashboards and exposed API endpoints using FastAPI for seamless integration with internal tools. Led the transition to LLM-powered solutions, designing and deploying a clinical document intelligence system utilizing GPT-4 APIs, LangChain , and Retrieval-Augmented Generation (RAG) for enhanced summarization and decision support. Integrated FAISS and Weaviate vector databases for semantic search, enabling efficient retrieval of contextually relevant medical information. Engineered prompt templates and optimized ( LLM ) Large Language Models responses through advanced prompt engineering strategies tailored for healthcare-specific use cases. Developed a speaker identification and transcription enhancement system using transformer-based models, improving multi-speaker clinical note processing. Containerized LLM workflows using Docker, with deployments across AWS SageMaker and Azure ML Studio to ensure scalable and secure access to AI services. Employed MLflow for model versioning and experiment tracking, supporting reproducibility and collaborative development in multi-cloud environments. Designed and maintained data validation and quality assurance workflows using Pandas, Great Expectations, and custom Python scripts, ensuring integrity across M achine Learning and LLM pipelines. Fine-tuned domain-specific Transformer models (e.g., BioBERT , ClinicalBERT ) using Hugging Face Transformers, enhancing performance on specialized clinical text tasks. Implemented scalable data pipelines on AWS using S3, Glue, and Athena for structured and semi-structured healthcare datasets, enabling efficient querying and model input preparation. Developed custom vectorization and embedding techniques with SentenceTransformers and BERT-as-a-Service to improve semantic understanding in document retrieval tasks. Automated model retraining workflows using Apache Airflow, enabling continuous integration of new data and adaptive learning for deployed ML and LLM models , while fostering a collaborative and solutions-oriented team environment .
Job description
Tools & Tech: Python, R, Scala, SQL, Pandas, NumPy, Dask, scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, OpenCV, spaCy, NLTK, Tableau, Power BI, Matplotlib, Jenkins, Docker, Git, PySpark, Hadoop, Hive, Kafka, Apache Airflow, PostgreSQL, MySQL, MongoDB, Cassandra, AWS (S3, EC2, Glue), Azure ML Studio
Key Responsibilities:
• Developed machine learning models to enhance the bank’s loan approval system, leveraging customer demographics, credit scores, and financial history to improve predictive accuracy and reduce default rates.
• Designed scalable model pipelines in Python and validated statistical assumptions in R processed high-volume transactional data using Scala and PySpark across Hadoop-based distributed systems.
• Performed advanced feature engineering, data cleaning, and dimensionality reduction using Pandas, NumPy, Dask, and scikit-learn for large-scale datasets.
• Applied XGBoost, LightGBM, and CatBoost for ensemble learning, enhancing classification accuracy and stability across imbalanced datasets.
• Experimented with TensorFlow and Keras for deep learning models focusing on customer churn prediction and unstructured document classification tasks.
• Built OCR-based ingestion workflows using OpenCV, combined with spaCy/NLTK for text extraction, named entity recognition (NER), and document classification.
• Designed and maintained business intelligence dashboards in Tableau and Power BI, visualizing key KPIs for credit risk, churn, and product performance.
• Created data visualizations using Matplotlib and Seaborn for model diagnostics, including confusion matrices, ROC curves, and feature importance plots.
• Developed streaming and batch ETL pipelines with Kafka and Apache Airflow, enabling real-time and scheduled data integration from various banking systems.
• Deployed ML solutions using AWS (S3 for storage, EC2 for compute, Glue for ETL), and explored model deployment alternatives with Azure ML Studio.
• Managed hybrid data environments across relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) databases for diverse data types.
• Implemented CI/CD pipelines with Jenkins for continuous integration, automated testing, and deployment of containerized ML applications via Docker.
• Used GitHub and Bitbucket for source control, managing collaborative development and versioning of production-ready Machine Learning models.
• Coordinated sprint planning and deliverable tracking using Jira, collaborating with cross-functional teams including data engineers, analysts, and business stakeholders.
• Participated in risk model governance reviews, preparing documentation and model artifacts for internal audit and compliance verification.
Job description
Tools & Tech: Python, SQL, Google Cloud Platform (BigQuery, Dataflow), Pandas, NumPy, Seaborn, Matplotlib, SciPy, Scikit-learn, TensorFlow, ARIMA, Random Forest, Gradient Boosting, Flask, Tableau, Power BI
Roles&Responsibilities
• Developed and deployed data pipelines using Google Cloud Dataflow to ingest sensor and survey-based customer data from real estate and retail assets into BigQuery, supporting real-time analytics and trend forecasting.
• Led the design of dynamic pricing strategies, implementing A/B testing for two product lines that resulted in a 20% increase in conversion rates, using insights from historical purchase patterns and customer feedback.
• Created predictive models using ARIMA and TensorFlow to forecast demand for retail assets and housing-related services, enabling proactive stock and resource management.
• Designed a local search optimization algorithm using dynamic programming in Python, improving the speed of logistics-related computations by 23%, enhancing the efficiency of product delivery planning.
• Built statistical models including Multivariate Regression, Logistic Regression, Random Forest, and Gradient Boosting to analyze customer behavior, segment markets, and support targeted marketing initiatives.
• Wrote complex SQL queries for customer data extraction and time-series validation, managing datasets of over 10 million rows across various sources, enabling accurate KPIs for business units.
• Collaborated with cross-functional teams in engineering, marketing, and sales to redesign customer engagement strategies based on insights from Exploratory Data Analysis (EDA).
• Conducted feature engineering, data cleaning, and scaling using Pandas, NumPy, and SciPy, ensuring high-quality input for machine learning pipelines.
• Developed interactive dashboards in Tableau and Power BI to visualize customer trends, pricing outcomes, and forecasting results for executive stakeholders.
• Served analytical outputs through Flask APIs, supporting integration with internal business tools for real-time access to predictive insights. Performed topic modeling using LDA (Latent Dirichlet Allocation) to identify emerging themes in customer feedback, supporting product innovation.
• Led root cause analysis initiatives for low-performing products, applying decision tree analysis and delivering actionable insights for product refinement.
• Built data quality monitoring scripts to validate incoming data streams, reducing errors and improving overall pipeline reliability.
• Implemented automated alert systems for anomalies in sales trends, using time-series anomaly detection techniques to flag significant deviations.
• Contributed to data governance policies, ensuring compliance with data privacy standards and best practices in customer data handling.
• Conducted market basket analysis to identify purchasing patterns, informing cross-sell and upsell strategies across different store regions.
• Improved financial market prediction accuracy by 20% using XGBoost, SVM, and Random Forest models, directly influencing investment strategies.
• Reduced financial reporting time by 30% by developing optimized SQL and Python queries and stored procedures, enabling real-time analytics for a team of 10+ analysts.
• Empowered a team of 5+ executives with actionable insights through interactive Power BI dashboards, directly impacting strategic financial decisions.
• Automated data integration pipelines, processing millions of transactions daily and supporting real-time decision making across multiple financial platforms.
• Increased customer retention by 15% within one year by applying NLP to analyse customer feedback and optimize satisfaction efforts.
• Enhanced generative AI model performance by 10% through integrating data quality monitoring into CI/CD pipelines, ensuring consistent training data.
• Generated $2.5 Million in revenue by implementing A/B testing optimized financial product features, directly impacting customer interaction metrics.
• Deployed machine learning models via CI/CD pipelines, ensuring continuous improvement and reliability for financial forecasting applications.
• Improved financial market prediction accuracy by 20% using XGBoost, SVM, and Random Forest models, directly influencing investment strategies.
• Reduced financial reporting time by 30% by developing optimized SQL and Python queries and stored procedures, enabling real-time analytics for a team of 10+ analysts.
• Empowered a team of 5+ executives with actionable insights through interactive Power BI dashboards, directly impacting strategic financial decisions.
• Automated data integration pipelines, processing millions of transactions daily and supporting real-time decision making across multiple financial platforms.
• Increased customer retention by 15% within one year by applying NLP to analyse customer feedback and optimize satisfaction efforts.
• Enhanced generative AI model performance by 10% through integrating data quality monitoring into CI/CD pipelines, ensuring consistent training data.
• Generated $2.5 Million in revenue by implementing A/B testing optimized financial product features, directly impacting customer interaction metrics.
• Deployed machine learning models via CI/CD pipelines, ensuring continuous improvement and reliability for financial forecasting applications.
Job description
• Led A/B testing with product and engineering teams, ensuring data science models delivered measurable business value and met quality standards.
• Refined ETL workflows with SQL, streamlining data extraction and cleansing for improved analysis and reporting for a team of 5+ data engineers.
• Delivered actionable insights via Tableau dashboards, informing strategic decisions on consumer behaviour and sales trends for a 10+ member executive team.
• Implemented prediction accuracy by 15% by developing and fine-tuning machine learning models (SVM, Decision Trees, Neural Networks) for customer churn and product recommendations.
• Enhanced operational efficiency by 20% through developing Deep Learning models (RNN, LSTM) with Keras, supporting industrial decision-making processes.
• Migrated analytics databases from Redshift to Databricks, increasing system productivity by 30% and optimizing cloud computing resources.
Recent position
08/01/2023
-
05/02/2025
Skills
MySQL
Data Extraction
Streamlining
Data Analysis
Management
Visualization
Analysis Of Variance (ANOVA)
Time Series
Cloud Migration
Exploratory Data Analysis
Pattern Recognition
Database Management
Github
Data Storytelling
Power BI
Analytics
Recurrent Neural Network (RNN)
Deep Learning
Machine Learning
Reliability
Tableau (Business Intelligence Software)
Keras (Neural Network Library)
Data Science
Python (Programming Language)
Customer Retention
Forecasting
Sales
SQL (Programming Language)
Financial Forecasting
Xgboost
Data Transformation
Extract Transform Load (ETL)
Query Optimization
Quality Monitoring
Decision Making
Data Architecture
Data Visualization
Business Intelligence
PostgreSQL
Data Presentation
Predictive Analytics
Seaborn
NoSQL
Google Data
Matplotlib (Python Package)
Electronic Design Automation (EDA) Software
Storytelling
Transformation (Genetics)
Statistical Analysis
Statistics
Quantitative Analysis
Operational Efficiency
Data Integration
Long Short-Term Memory (LSTM)
Data Quality
Cloud Computing
Consumer Behaviour
Financial Market
Databricks
Testimonials from Our Customers
Gary Volochinsky
ITLC Manager | Medical Diagnostic Laboratories LLC
I am very pleased with the whole process and services Axiom has provided during our search for ITLC Support Specialist. Hiring Anzhelika was a very good choice, as she has been able to learn and follow MDL work flow and became a great asset to the ITLC team in a short period of time. I will definitely utilize your services for any future positions needs.
Sarah Landenwitsch
Vice President & Managing Member | STATKING Clinical Services
I appreciate everything you've done to help us so far. This process has been very easy. I have nothing but positive things to say about Vigen. He has been a great addition to our team. He’s reliable and enthusiastic when it comes to working on any project that I have given him. Vigen has also made suggestions that we will look to implement here as well. Overall, I am very pleased to have him on our team.
Charu Trikannad
Director of IT – Business Applications
I am happy to inform you that Nanda is excellent and is passionate about the work assigned to her. Communication, collaboration is perfect and excellent analytical skills. She understands relationships between entities and has drawn ER Diagrams. It’s a pleasure working with her. Hope it continues that way.
Angelique Munoz
IT Coordinator | Information Technology Department | Genesis Biotechnology Group
We are extremely satisfied with the team that you have provided to us! They are doing exceptionally well.
Jason West-McReynolds
Co-Founder | Fran Metrics
We are extremely happy with the service and results from Axiom Pro. Arman has been a great addition to our team, and we are happy to have him here.
Jose Abadi
Ecommerce and ERP Solutions Architect | Firecommerce
Axiom recruitment service provided the quality of candidate I needed for a personal assistant. The cultural fit is perfect as much as the skills and experience required for the job. I appreciate the dedication and speed with which they delivered this job!
Alex Greenberg
Managing Partner | Vivat, Inc.
Exceptional Recruiting Firm – Highly Recommended!
We urgently needed to find qualified resources for a strategic initiative. Axiom Pro quickly became an invaluable partner in our quest to find top-tier talent for our company. Their highly personable, professional, and responsive team has shown us their dedication to understanding our hiring needs. They took the time to thoroughly vet candidates, ensuring we only met with the best fit for our organization.
Thanks to Axiom Pro, we discovered fantastic people who seamlessly blended into our group and made a powerful impact. Axiom Pro's unique approach to the recruitment process, which combines personalized techniques with GenAI capabilities, truly sets them apart from other firms, making them an exclusive choice for top-tier talent.
If you're looking for a dedicated, creative, reliable, and trustworthy recruiting firm that delivers quality candidates and exceptional service, I can't recommend Axiom Pro highly enough!
Boris Bogomolnik
Chief Information Officer | IT Strategy & Digital Transformation | Business-Aligned Technology Leader at Genesis Global Group.
We have been working with Axiom Pro for over 8 years, leveraging their expertise to augment our in-house IT team. This partnership has been instrumental in enabling us to bring new products and services to market faster, while maintaining exceptional quality.
Axiom Pro has played a key role in our success by helping us create a highly effective hybrid model, providing access to top-tier IT talent including Business Analysts, Developers, Architects, QA specialists, and Release Managers.
As a multi-vertical company, we manage and support a diverse portfolio of applications across different industries. In just the last year, with Axiom Pro’s support, we successfully developed and deployed a custom AI application—further accelerating innovation and expanding our capabilities.
Axiom Pro has consistently demonstrated the ability to assist in most areas of our operations, providing reliable, skilled, and scalable resources when we need them most. Our long-term collaboration with Axiom Pro has been extremely productive, and we are very satisfied with the outcomes. They are more than just a vendor—they are a trusted partner in our growth and innovation.
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About AXIOM PROWho we are and how we help you hire smarter
AXIOMPRO is your strategic partner for hiring top global talent.
We specialize in recruiting and outstaffing solutions that help you scale efficiently — without the overhead.
Our team rigorously pre-screens candidates, verifies English fluency and technical proficiency, and presents only job-ready professionals who can hit the ground running.
