Senior Data Scientist - Discount Tire
(2023-10)
Led strategic analytics initiatives supporting product and business stakeholders, translating large-scale customer, transaction, and operational datasets into actionable insights influencing executive decision-making.
- Developed customer behavior and conversion analytics models using Python, SQL, NumPy, Pandas, SciPy, and Scikit-learn, improving understanding of customer engagement patterns and purchase drivers.
- Designed and executed A/B tests and experimentation frameworks to evaluate customer experience initiatives, quantify business impact, and support data-driven product decisions.
- Applied statistical analysis and causal inference methods to estimate incremental value generated by marketing campaigns, promotional strategies, and customer engagement programs.
- Built predictive models for customer retention, demand forecasting, and revenue optimization, enabling proactive business planning and increased operational efficiency.
- Created executive dashboards and KPI frameworks using Power BI and Looker to monitor product performance, customer lifetime value, conversion rates, and business growth metrics.
- Partnered closely with product managers, engineering teams, and business leaders to define analytics strategy, establish OKRs, and identify opportunities for growth and process optimization.
- Analyzed billions of records using distributed computing frameworks and BigQuery, delivering scalable solutions for enterprise-wide business intelligence and analytics.
Data Scientist - Dell
(2022-08 - 2023-10)
Developed and deployed hybrid AI/ML solutions and predictive models using Python and AWS services, supporting large-scale analytics across customer behavior, risk assessment, and product performance domains.
- Built data ingestion and transformation pipelines leveraging AWS Glue, Lambda, and distributed storage systems, handling large volumes of structured and semi-structured data for downstream analytics and modeling use cases.
- Designed feature engineering pipelines and data preprocessing frameworks to handle noisy, incomplete, and unstructured data inputs, enabling robust model training and reliable predictions.
- Implemented advanced analytics techniques including classification models, clustering, and statistical methods, driving insights from large-scale enterprise datasets with millions to billions of records.
- Collaborated with engineering and product teams to integrate and deploy models into production environments, ensuring scalability, performance, and maintainability of deployed solutions.
- Created automated reporting and monitoring systems for model performance and data quality, ensuring continuous improvement and reliability of analytics pipelines.
- Designed experiments and validation processes to evaluate model performance and improve predictive accuracy, leveraging statistical foundations and continuous monitoring.
- Conducted large-scale product and customer analytics using Python, SQL, Pandas, NumPy, AWS analytics services.
- Designed statistical models to evaluate customer behavior, product adoption, retention, and revenue performance.
- Implemented experimentation frameworks and hypothesis testing methodologies to assess the effectiveness of product enhancements and customer engagement initiatives.
- Built automated reporting systems and executive dashboards that delivered actionable business insights across product, operations, and leadership teams.
- Collaborated with cross-functional stakeholders to prioritize analytical opportunities and influence strategic product decisions.
Data Scientist (Big Data, Distributed Systems & Data Processing) - Travvir
(2021-08 - 2022-06)
- Built large-scale data processing pipelines using Apache Spark, Hadoop, and distributed computing frameworks, enabling efficient processing and transformation of high-volume datasets across enterprise systems.
- Designed and optimized ETL pipelines for ingesting structured and unstructured data from multiple sources, ensuring seamless integration into centralized data platforms.
- Developed data transformation and standardization workflows to improve data quality, consistency, and usability for downstream machine learning and analytics applications.
- Implemented data validation, monitoring, and logging frameworks, ensuring robustness and reliability of large-scale data workflows operating in production environments.
Data Scientist (Advanced Analytics, ETL & Data Warehouse Systems) - PolicyBazaar
(2019-09 - 2021-08)
- Designed and implemented enterprise-grade data pipelines and data warehouse architectures for integrating large-scale, multi-source datasets, including customer data, policy data, and external vendor inputs.
- Built and deployed predictive and statistical models for business analytics, leveraging structured and semi-structured data to drive insights into customer behavior, risk assessment, and operational performance.
- Developed data extraction and preprocessing systems, handling complex data formats and ensuring transformation into structured schemas suitable for reporting and modeling.
- Performed data enrichment and integration across multiple datasets, including customer demographics, transactional data, and external data sources, enabling comprehensive analytical views.
- Delivered data-driven insights through dashboards and business intelligence tools, supporting decision-making across finance, operations, and product teams.
- Performed customer, transaction, and risk analytics across insurance and financial services products, supporting strategic business and product decisions.
- Developed predictive models for customer acquisition, retention, policy conversion, and revenue optimization.
- Applied statistical analysis, segmentation techniques, and propensity modeling to understand customer purchasing behavior and improve conversion rates.
- Conducted cohort analysis, funnel analysis, and lifetime value analysis across millions of customer transactions.
- Delivered executive-level recommendations through data storytelling, dashboards, and presentations that influenced product and operational strategy.
Data Scientist (Business Analytics, Forecasting & Optimization) - SilverEdge Technologies
(2017-12 - 2019-09)
- Developed predictive forecasting models and optimization algorithms for pricing, demand forecasting, and revenue growth, leveraging statistical analysis and machine learning techniques.
- Applied advanced analytics and data mining techniques to identify revenue opportunities, operational inefficiencies, and customer behavior patterns, leading to improved business outcomes.
- Built customer segmentation and behavioral analytics models, enabling targeted marketing strategies and increasing revenue by approximately 22% within a short timeframe.
- Conducted cost optimization and operational efficiency analysis, leveraging data-driven insights to reduce operational expenditures by approximately 32%.
- Delivered consulting-style analytical insights, stakeholder presentations, and business recommendations, supporting strategic decision-making across client organizations.
Data Analyst - Elation EdTech
(2016-12 - 2017-01)
- Performed data extraction, cleaning, and statistical analysis using SQL, Python, and BI tools, supporting business reporting and analytics initiatives.
- Developed interactive dashboards and reports using Power BI, enabling stakeholders to track key performance metrics and operational efficiency.
- Assisted in building data pipelines and reporting frameworks, improving data accessibility and reporting efficiency across teams.
