Looking for a job

Sachin User

@sachinuser29

Senior Data Scientist at Aditya Birla Group (Mobcoder), built a buyer-retention model with 0.90 ROC-AUC and 88% accuracy.

India

Message

At Aditya Birla Group (Mobcoder), I built an XGBoost buyer-retention model that predicted monthly return likelihood with 0.90 ROC-AUC and 88% accuracy. I also used seller-level analytics and prioritization dashboards to support recovery actions that reduced interest cost from ₹27 Cr to ₹18 Cr.

At Mu Sigma Inc., I led an 8-member data science team delivering retail audience-selection solutions, and built experimentation and model-performance frameworks that drove approximately 25% uplift in campaign engagement. Earlier, at Bosch Global Software Technologies, I developed pricing and inventory optimization models that improved inventory turnover by approximately 20% and reduced stockout/overstock incidents by 15–18%.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Scientist

Current

Aditya Birla Group (Mobcoder)

Apr 2026 - Present (6 months)

Built an XGBoost buyer-retention model predicting monthly return likelihood, achieving 0.90 ROC-AUC and 88% accuracy. Led RCA of delayed invoicing and spearheaded digital marketplace analytics, including acquisition funnels and RFM segmentation.

XGBoost Machine Learning Python SQL Power BI Tableau Excel Statistical Modeling EDA Time series Forecasting A B Testing Causal Inference

Data Science Team Lead and Data Scientist

Mu Sigma Inc.

Aug 2025 - Apr 2026 (8 months)

Led an 8-member data science team delivering end-to-end retail audience-selection solutions. Architected propensity, churn, and next-best-action models, and built experimentation frameworks driving approximately 25% uplift in campaign engagement.

Python SQL Power BI Machine Learning EDA Feature Engineering A B Testing Uplift Modeling Team Leadership Stakeholder Management

Data Scientist

Bosch Global Software Technologies

Jan 2020 - Jul 2025 (5 years 6 months)

Built a linear regression model for vehicle resale values, achieving 0.86 R² and reducing valuation turnaround by ~45%. Developed pricing and inventory optimization models, improving inventory turnover by ~20% and reducing stockout incidents by 15–18%.