Sep 2023 – Dec 2023 · 3 mos

• Courses : Intro to Data Science , Intro to Big Data
• Collaborated with faculty members to enhance the curriculum, incorporating real-world examples and case studies to enrich the educational experience based on my industry experience
• Offered one-on-one doubt sessions to students during my office hours, grading exam papers and projects addressing individual learning needs

Jun 2023 – Sep 2023 · 3 mos · Remote

• Advance Analytics:
• Analyzed profitable investment decision making by comparing performance of leading taxi service providers using advance analytics methods; performed EDA, correlations, data wrangling, A/B testing, regression using Python and SQL; delivered automated business insights dashboard in Tableau that reduced decision making time by 50% for client.
• Details :
• Performed Exploratory Data Analysis (EDA) using Python (matplotlib and seaborn libraries) to help analyze distributions, outliers and data trends.
• Conducted in-depth analysis of 350k record dataset using pandas. Found correlations between variables to understand significant variable to be used in modeling
• Constructed master table using optimized SQL queries; included relevant variables after cleaning data
• Executed hypothesis testing (2 sample t-tests), to rigorously validate certain variable relationships and identify robust predictor variables
• Constructed predictive models using Linear Regression, Decision Tree, and Random Forest algorithms; achieved better performance metric through ensembled model using it to predict better investment choices

Dec 2021 – Apr 2023 · 1 yr 4 mos · Remote

• Marketing Investment Optimization:
• Led a team of 2 analysts to implement an end to end Marketing Mix Model (MMM) using SQL and Python for project worth USD 100k for a fortune 100 healthcare pharma client; help client understand historical channel performance (ROI and mROI) and help optimize marketing budget for next cycle leading to 17% boost in overall sales
• Details:
• Gathered requirement from cross-functional teams and clients to make concise report for technical implementation
• Designed and implemented a Retail Marketing Model using Python, integrating diverse media channels and control variables
• Developed predictive sales models utilizing media impressions/spending, emphasizing key metrics like ROAS and mROAS for retail clients
• Implemented ad stock transformation to assess lag effects in media channels, optimizing strategies and increasing sales efficiency
• Pricing Analytics:
• Developed optimal pricing benchmark model to maximize revenue by examining competitive landscape for a leading e-commerce client; Designed BI tools and automated processes to analyze and quick response to dynamic pricing changes, using SQL, and Tableau, which support $2 billion revenue operations
• RFP and Strategy:
• Enhanced process for responding to Request for Proposals using Java Script, SQL and Python; reduce timely submissions by 50% and compliance with client requirements. Collaborated with cross-functional teams to gather information and tailored responses.

Jan 2021 – Nov 2021 · 10 mos

• Customer Analytics:
• Built robust regression models to identify target customer segments for newly launched credit card; proposed target customer segment list optimizing potential customer default loses to company by 15% and reducing target promotion cost for marketing team by 30%.
• Churn Prediction: Implemented predictive model to identify customers likely to churn based on customer activity and transaction data; helped reduce ∼10% credit card churn

Jul 2020 – Dec 2020 · 5 mos

• API Development:
• Facilitated easy storage and retrieval of data by developing REST APIs for Credit Risk Private Banking application (dashboard to consolidate all user applications at one location) using Spring Boot and SQL; increased response time by 15%.
• UI Development:
• Increased efficiency by 20% by implementing multiple UI enhancements and design changes based on user feedback. Tech used: ReactJs.
• Micro Services POC:
• Implemented a proof of concept (POC) using Spring Boot with Messaging System Kafka; demonstrated use cases of POC within project showcasing benefits.
• Analytics Trainer:
• Enhanced internal training materials for SQL and Python courses; Conducted technical trainings for new analysts and facilitate successful onboarding and project readiness.

Jun 2019 – Jul 2019 · 1 mo · Bengaluru, Karnataka, India · On-site

• - Implemented a POC using Spring Boot with Messaging System Kafka; demonstrated the feasibility of using Kafka for high-performance messaging by processing over 10000 messages per second with an average latency of less than 10 milliseconds