Jul 2024 – Jul 2025 · 1 yr · Hyderabad, Telangana, India

• Optimized product success metrics for anchor delivery models, driving a 5% reduction in delivery time for walker routes and elevating overall delivery efficiency
• Identified underreported app crashes through metrics analysis, leading to a 3% increase in weekly deliveries by enhancing app stability and performance tracking

May 2023 – Jul 2024 · 1 yr 2 mos · Hyderabad, Telangana, India · On-site

• Created a safer platform through geofence nudges, reducing incident rates by 19% in six months, through proactive active surveillance
• Mitigated risks by proactively profiling platform users, resulting in a 5% decrease in driver-reported incidents, enhancing the effectiveness of spender actioning mechanism
• Elevated product safety KPIs with a data-driven strategy, crafting an interactive dashboard employing hypothesis testing, resulting in a notable 10% reduction in serious physical altercations

Jan 2022 – May 2023 · 1 yr 4 mos

• Worked on several analytical initiatives with the world's leading Home Improvement retailer, a Fortune 50
• Developed a predictive learning approach for a hyperlocal marketing campaign initiative to identify best performing stores to position store associates, resulting in a nearly fourfold increase in incremental revenue per customer using propensity model
• Developed numerous machine learning propensity, revenue models with XGBoost to efficiently target ~60M customers across multiple campaigns, resulting in a ~50M attributable revenue
• Utilized unsupervised machine learning technique, numerous statistical methodologies and predictive analytics to determine optimum look-alike audiences for different stakeholders
• Developed several novel machine learning propensity, revenue models using XGBoost to efficiently cater ~60M customers for efficiently targeting customers across multiple campaigns, resulting in ~$50M attributable revenue
• Built a tableau dashboard to track the health and performance of the deployed models using various model indicators

Jan 2021 – Dec 2021 · 11 mos

• Worked with a Fortune 10 Pharmaceutical Giant in an R&D team
• Performed survival analysis for clinical trials to get actionable results, and examined patients' deteriorating conditions to identify those most likely to experience an event using random forest classifier
• Leveraged Isolation Forest anomaly detection algorithm to build several ML pipelines to detect anomalies in clinical data and hence reduced manual reviewing efforts

Sep 2020 – Jan 2021 · 4 mos

• Successfully completed 4 months of MSU training, focused on problem solving journey across retail , Oil & Gas, Technological and Pharmaceutical industries for Fortune 500 companies.
• Ranked 1st among 450+ Apprentices of MSU batch 2020 as part of MSU TrailBlazers Programme.
• Used decision sciences to solve real world problems using tools such as Python, PowerBi, Excel. SQL.