Headline

Decision Scientist @ Mu Sigma | Python, SQL, Predictive Analytics | Turning data into business decisions

About

Turning customer data into smarter business decisions is what separates a good campaign from one that delivers real impact. I’m a Decision Scientist specializing in retail and loyalty analytics, helping business teams understand customer behavior, improve campaign targeting, and measure what truly drives performance. At Mu Sigma, I’ve worked on large-scale customer datasets and loyalty marketing campaigns, using SQL, Snowflake, Python, Power BI, and Excel to transform complex data into actionable insights. Here’s where I add value: 1. Customer Analytics: Develop customer segmentation and targeting strategies based on transactional and behavioral patterns. 2. Campaign Effectiveness: Analyze campaign performance through redemption rates, sales uplift, incremental revenue, and customer engagement. 3. Data-Driven Decision Making: Build analytical reports and dashboards that help stakeholders evaluate performance and identify opportunities for improvement. 4. Business Problem-Solving: Translate business requirements into analytical solutions through structured thinking and cross-functional collaboration. I enjoy working at the intersection of data, technology, and business—where the goal isn’t just to report what happened, but to understand why it happened and what should happen next. I’m always keen to connect with professionals working in data analytics, decision science, customer intelligence, and business strategy. Let’s connect and explore how data can drive better decisions and measurable business outcomes.

Location

Mumbai

AI stack

claude code

claude

deepseek

gemini

openai

ChatGPT

Work experience

Trainee Decision Scientist at Mu Sigma Inc., Jul 2024 - Oct 2026 · 2 yrs 4 mos, Bengaluru, Karnataka, India, 1. Analyzed multi-million-row transaction datasets for one of Australia's largest Loyalty Program using complex SQL aggregations to answer business questions and produce the audience and measurement inputs used for retail partner reporting. 2. Identified trends and drivers of campaign response in customer behavior data, contributing to a ~8% uplift in targeted marketing response rates. 3. Cut manual reporting effort by ~40% by automating recurring reports, improving the speed and consistency of performance monitoring. 4. Measured incremental impact on sales, transactions and engagement through test-and-control and A/B analysis, turning results into recommendations to improve the next campaigns metrics. 5. Created behavior-based customer segments using SQL on Snowflake, giving campaign teams clearly defined audiences for targeting. 6. Introduced automated trigger campaigns based on customer inter-purchase intervals (IPI), activating targeted communications when customers reached defined purchase thresholds to encourage repeat shopping. 7. Partnered with business users to define KPIs, validate data and interpret results, improving trust in reporting.

Education

Bachelor of Technology - BTech, Vellore Institute of Technology