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Senior Analyst with 2+ years in Data Analysis, SQL & Machine Learning
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Highly analytical and detail-oriented professional with 1 year of experience in data analysis, business intelligence, and AI/ML. Proven ability to transform complex data into actionable insights, automate reporting processes, and drive significant improvements in revenue recovery, operational efficiency, and strategic decision-making through SQL, Python, PowerBI, and advanced statistical modeling. Strong background in healthcare claims analysis and HR data analytics.
AISSMS Institute of Information Technology
Bachelor of Engineering · Artificial Intelligence and Data Science
February 1, 2021 – May 1, 2025
Cognizant
Programmer Analyst Trainee
March 1, 2025 – Present
Pune, Maharashtra, India
iMocha
Business Analyst Intern
August 1, 2024 – March 1, 2025
Pune, Maharashtra, India
Pricing & Promotion Effectiveness Analysis
June 17, 2026 – Present
• Uncovered $2.1M in wasted promotional spend by analyzing 2.8M retail transactions across 30 campaigns found that 17% of campaigns generated zero incremental demand due to pantry loading (customers stocked up during promo, stopped buying after) • Proved that discounts beyond 20% destroy margin, the 30%+ discount band showed, 64% revenue loss, while the 1-10% band delivered 36% sales lift, directly leading to a recommendation to cap all promotions at 15% • Discovered display promotions deliver 3x higher ROI than mailers (0.14x vs -0.04x), yet 60% of budget was allocated to mailers, recommended budget reallocation projected to triple promotional returns • Segmented 801 households using RFM analysis and K-Means clustering, revealed that high value customers don't need discounts (+0.34% lift), while at-risk customers don't respond to them (-0.37%), eliminating wasteful blanket discounting • Built complete analytics infrastructure: dbt pipeline (14 models, 28 tests) on Snowflake, statistical validation in Python (t-tests, DiD, K-Means), interactive Power BI dashboard with What-If simulation, and Excel ROI calculator for marketing team to model scenarios before launching campaigns
View ProjectAPI-Augmented Reinforcement Learning Framework
June 17, 2026 – Present
• Built an API-driven reinforcement learning framework that optimized portfolio allocation across 50+ stocks improved risk adjusted returns by 15% compared to baseline buy and hold strategy • Ran sentiment analysis on 1,000+ financial news articles using NLP, converting unstructured text into quantifiable market signals that fed directly into the portfolio decision engine • Combined sentiment scores with historical price data to create a hybrid model that outperformed pure technical analysis, the sentiment layer alone accounted for 8% of the total return improvement.
View ProjectCultural Fit Analysis
The candidate's project diversity, ranging from healthcare claims analysis to retail promotion effectiveness and financial portfolio optimization, indicates a broad interest and adaptability to different business domains. Their proactive approach to identifying and solving problems, as well as their focus on delivering measurable business impact, suggests a good fit for a performance-oriented culture. The use of various technologies and methodologies (SQL, Python, ML, BI tools) shows a willingness to learn and apply new skills. The academic projects, while impressive, suggest a strong theoretical foundation that needs to be balanced with more extensive real-world, corporate experience to fully align with a senior analyst role.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving abilities, attention to detail in data validation, and a proactive approach to identifying and resolving inefficiencies. Their experience in communicating complex data to senior stakeholders indicates good presentation and interpersonal skills. The ability to work on diverse projects (healthcare claims, retail promotions, financial portfolio optimization) suggests adaptability and a strong learning aptitude. The candidate's focus on quantifiable impact aligns well with a results-driven environment.