Hemanthkumar E

Data Scientist

Mumbai, Maharashtra, India6 yrs 7 mos experience

Key Highlights

Expert in data science and predictive modeling.
Proven track record in improving financial metrics.
Strong background in machine learning applications.

Stackforce AI infers this person is a data science expert with a focus on Fintech and SaaS industries.

Contact

Skills

Core Skills

Data AnalysisPredictive ModelingSqlMachine Learning

Other Skills

Feature EngineeringGap AnalysisXGBoostMicrosoft OfficeMicrosoft ExcelMicrosoft WordMicrosoft PowerPointr programmingTableauHadoopDeep LearningBig Data AnalyticsA/B TestingLogistic RegressionK-Nearest Neighbors (KNN)

Experience

6 yrs 7 mos

Total Experience

1 yr 5 mos

Average Tenure

10 mos

Current Experience

Meesho

Data Scientist 3

Aug 2025 – Present · 10 mos · Bengaluru, Karnataka, India · On-site

Navi

Data Scientist

Aug 2024 – Aug 2025 · 1 yr · Greater Bengaluru Area · On-site

Dream11

Decision Scientist

Mar 2022 – Aug 2024 · 2 yrs 5 mos

Mastercard data & services

2 roles

Consultant

Jun 2021 – Mar 2022 · 9 mos

1) Contactless Card Adoption Strategy
 Devised strategies for implementing the contactless card adoption for one of the banks in US
 Engineered 200+ features based on RFM variables; calculated propensity score for each cardholder
 Observed a net spend lift of $280 per card per month leading to substantial additional revenue
2) Approval Rate Improvement Project
 Worked for one of the top acquirer banks in Australia to improve approval rate & reduce fraud rate
 Worked on preparing the data, performed gap analysis, opportunity sizing & final recommendation.
 Helped increase approval rate decrease fraud rate; sell some of the flagship products of MasterCard

Intern

Dec 2020 – May 2021 · 5 mos

Infosys

Associate Analyst - Data science

Oct 2017 – May 2019 · 1 yr 7 mos · Bengaluru, Karnataka, India

Primarily worked on marketing analytics project
1) Customer lifetime value modeling
 Built a ML model to predict customer spend for 2 million customers for next three months
 Engineered 30+ features based on RFM analysis, customer profile, events and browsing activity
 Built two step model ; one using Pareto/Negative binomial distribution for customer attrition and
Second using Xgboost model for customer value prediction using different purchase frequency
2) Sales Uplift modeling
 Predicted sales uplift for two promotional offers by assigning uplift score for each of the customer
 Assigned 2 classes for each customer based on control and treatment group and applied XGBoost
 Achieved 10% Incremental Response Rate(IRR) & Net Incremental Revenue(NIR) of $million