Data Scientist with 5+ years in Predictive Modeling & Business Intelligence
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Data Scientist with 4.5+ years of experience in predictive and risk modeling, including scorecard model development and validation. Strong in SQL and Python for data analysis, exploratory data analysis (EDA), and feature engineering. Experienced in delivering end-to-end insights through Power BI dashboards, enabling business performance improvement and supporting targeted interventions.
Yashvantrao Chavan Institute of Science, Satara
Master of Science · Statistics
January 1, 2018 – January 1, 2020
Shivaji University, Kolhapur
Bachelor of Science · Statistics
January 1, 2015 – January 1, 2018
Bajaj Life Insurance Ltd.
Deputy Manager - Analytics
November 1, 2024 – Present
Pune, Maharashtra, India
Schneide Solutions Pvt. Ltd.
Data Scientist
January 1, 2022 – November 1, 2024
Pune, Maharashtra, India
Spectrum Consultants India Pvt. Ltd.
Process Executive
April 1, 2021 – September 1, 2021
Pune, Maharashtra, India
13th Month Persistency (Collection) Scorecard (Insurance Premium Default / 45+ DPD)
November 1, 2024 – June 1, 2026
Developed and validated a 13th Month Persistency Scorecard using a GBM model to predict customers with high lapse/default propensity (Event Rate 30%), achieving a strong Kolmogorov-Smirnov (KS) statistic of 40. Enhanced model predictive power by incorporating granular data from Insurance Information Bureau of India (IIB) and Bureau Score, supplementing core demographic features via extensive EDA and Feature Engineering. Strategically optimized renewal campaigns by prioritizing the top 3 deciles, which captured 64% of the target non-persistent population, and improving premium collection effectiveness. Ensured model stability and reliability through robust self-validation using key metrics including Gain Chart analysis, Population Stability Index (PSI), and Characteristic Stability Index (CSI). Technologies Used: SQL, R, Python, Power-BI, Linux, Excel
Cross-Functional Business Intelligence Platform
January 1, 2022 – November 1, 2024
Developed and maintained Power BI dashboards integrating key marketing KPIs (such as ROI, CPL, and LTV) to enable data-driven decision-making, which allowed management to monitor campaign performance and optimize spend by quickly identifying top-performing and under-performing campaigns. Developed an interactive academic report in Power BI for tracking learners progress, allowing tutors to monitor and assess student performance. Designed and generated Power BI reports for operational efficiency, enabling the Renewal team to track payments and outstanding balances and assisting the Support team in prioritizing and responding to learner comments and reviews efficiently. Technologies Used : Excel, Microsoft SQL Server, MySQL, Power-BI, DAX, Data Modeling, Power-BI Service, Data Gateway
Propensity to Enroll Scoring System
January 1, 2022 – November 1, 2024
Developed and validated this model using Logistic Regression on an imbalanced dataset (Event Rate ≈ 5% of 250,000 users), achieving a strong Recall of 83% to ensure the prioritization of high-potential leads. Built Feature Engineering pipelines from granular behavioral logs (website activity, payment clicks) and demographic variables, then engineered and deployed a scalable MLOps pipeline on a Linux server for real-time lead scoring. The system provides daily lead scores from day one through the 7-day trial, allowing the Sales team to strategically prioritize high-propensity leads for targeted follow-up, directly optimizing resource allocation and course enrollment conversion. Technologies Used: SQL, Python, Linux
Power BI A-Z: Hands-On Power BI Training For Data Science!
Unknown
June 1, 2026 – Present
Introduction to Python by DataCamp
DataCamp
June 1, 2026 – Present
Programming for Everybody in Python by Coursera
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience spans insurance and education sectors, demonstrating adaptability. The projects involve direct business impact, aligning with a results-oriented culture. The breadth of tools and languages (SQL, Python, R, Power BI, Linux, Excel) indicates a willingness to learn and apply diverse technologies. The academic background in Statistics provides a strong theoretical foundation for data science roles.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong focus on delivering business value through data-driven insights and optimizing operational efficiency. The ability to translate complex models into actionable business strategies (e.g., optimizing renewal campaigns, prioritizing leads) suggests good problem-solving and business acumen. The experience with cross-functional BI platforms also points to collaborative skills.