TANMAY GAIKWAD
Data Scientist
Hyderabad,TG
Summary
Product-oriented Data Scientist with close to 3 years of experience specializing in the architecture of predictive and real-time models to drive growth, retention, and monetization in large-scale consumer products. Expert in applying statistical modeling (including Quantile Regression and XGBoost) and rigorous experimental design to translate complex behavioral data into high-impact business strategies. Proven track record in building probabilistic forecasting engines and recursive growth simulations that optimize multi-million dollar acquisition budgets and mitigate financial risk. Holds a Bachelor of Technology from IIT Bombay, providing a deep quantitative foundation for solving sophisticated data challenges.
Overview
3
3
years of professional experience
1
1
Certificate
Work History
Senior Product Analyst
Hitwicket
10.2025 - Current
- Probabilistic LTV Modeling: Engineered a multi-stage forecasting engine using Quantile Regression (L1 loss) to predict 180-day revenue multipliers based on D3 behavioral signals (VIP density, spending velocity). Integrated 10th, 50th, and 90th percentile intervals to account for heteroscedasticity and provide risk-adjusted breakeven windows.
- Conversion & Budget Optimization: Developed a probabilistic framework to estimate PUC30 (30-day Payers) from early D3 cohorts, enabling real-time CAC sensitivity analysis and dynamic scaling of acquisition budgets based on predicted LTV thresholds.
- Strategic Growth Modeling: Engineered a recursive, rule-based simulation engine to determine monthly acquisition spend required for a 15% MoM revenue uplift, accounting for cohort-level ARPMAU/retention (M0–M12+) and CPI incrementality to forecast 12-month budget and install targets.
- Experimental Design (A/B Testing): Spearheaded geo-specific pricing experiments for real-money currency; utilized hypothesis testing and ARPU uplift analysis to refine global monetization strategies.
Data Scientist
A23 Games (Head Digital Works)
04.2024 - 09.2025
- Developed a multi-stage retention modeling pipeline using supervised learning and behavioral clustering to predict Day-3 retention and enable targeted re-engagement strategies, improving retention by ~5%.
- Engineered an XGBoost model for a real-money gaming app to predict user purchase intent at game end event in real-time, capturing 85% of daily purchase events ($50M volume), enabling a dynamic "Offer Engine" that optimized promotional delivery based on individual user propensity scores.
- Designed wallet-flow simulations and rule-based models to evaluate user fund movement and liquidity dynamics in real-money gaming systems.
- Designed, executed, and analyzed A/B experiments on monetization and retention features, measuring impact and guiding feature rollout and cost optimization.
- Automated recurring analytical reporting pipelines using Python and SQL, significantly reducing manual effort and improving data reliability.
- Led analytical evaluation of KYC automation feature using funnel analysis, quantifying uplift across registration, activation, and conversion stages across 5+ products.
Associate Data Scientist
A23 Games (Head Digital Works)
06.2022 - 06.2023
- Leveraged SQL and statistical modeling to analyze high-volume user funnels and monetization metrics across real-money poker and rummy products, identifying key drivers of user $LTV$.
- Engineered granular, cohort-level retention frameworks segmented by stakes and game-type to provide data-driven recommendations for product scaling and stake configuration.
- Built automated SQL-based analytical workflows to quantify gameplay latency and identify systemic UX bottlenecks in time-to-game-start, leading to platform-wide configuration optimizations.
- Conducted revenue impact assessments on new game formats using engagement data, resulting in the strategic decommissioning of underperforming variants to maximize platform efficiency.
- Developed end-to-end automated pipelines to calculate Customer Acquisition Cost (CAC) and multi-channel performance, reducing manual reporting latency and improving budget transparency.
- Analyzed user wallet behavior and transactional patterns to detect bonus abuse and anomalous gameplay, supporting early-stage fraud mitigation and revenue protection initiatives.
Education
B.Tech - Chemical Engineering
Indian Institute of Technology Bombay
Mumbai, India08-2023
Skills
Machine Learning
Propensity Modeling
Time Series Forecasting
Hypothesis Testing
Python (pandas, numpy, scikit learn)
SQL (Advanced)
Feature Engineering
EDA
Hyperparameter Tuning
PowerBI
Tablue
Data Visualization
Statistical Analysis
Classification
Regression
Decision Trees
Report Automation
Deep Learning
Advanced Excel
Data Pipelines
Certification
Completed Specialization in Machine Learning from deeplearning.ai
Timeline
Senior Product Analyst
Hitwicket
10.2025 - Current
Data Scientist
A23 Games (Head Digital Works)
04.2024 - 09.2025
Associate Data Scientist
A23 Games (Head Digital Works)
06.2022 - 06.2023
B.Tech - Chemical Engineering
Indian Institute of Technology Bombay