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Data Engineer with 2+ years in Python, SQL & ML pipelines.
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Data Analyst with 2+ years of experience at Uber, Google, and ThinkMates, building automated reporting pipelines, KPI dashboards, LLM quality evaluation workflows, and commercial analytics systems. Reduced manual reporting effort across 22 global markets, improved annotation consistency in multi-annotator ML pipelines, and contributed to 61% revenue growth at a MedTech client. Skilled in Python, SQL, Pandas, Power BI, and Google Sheets.
VNR VJIET
B.Tech · Data Science
August 1, 2021 – June 30, 2025
Narayana College
Intermediate (MPC)
June 1, 2019 – May 31, 2021
Google (via Indium Software)
Data Analyst
September 1, 2025 – May 1, 2026
Hyderābād, Telangana, India
Uber (via Indium Software)
Data Analyst
August 1, 2024 – August 1, 2025
Hyderābād, Telangana, India
ThinkMates
Data Analysis Intern
May 1, 2024 – July 1, 2024
Hyderābād, Telangana, India
BULL'S EYE VIEW - UBER EATS COHORT & RETENTION ANALYSIS
June 18, 2026 – Present
Analysed 12 months of Uber Eats transaction data across 7 rolling sub-windows (1, 2, 4, 7, 10, 15, 30 days) to measure order frequency gaps between members and non-members. Constructed a SQL cohort system using SEQUENCE logic and 30-day windows to track order behaviour across 2 distinct user segments. Calculated retention KPIs by flagging full user retention segments across rolling sub-windows; findings fed into product engagement planning.
RF LABEL PREDICTOR - AI-POWERED ROOT FAILURE CLASSIFICATION
June 18, 2026 – Present
Developed an AI-driven Root Failure (RF) prediction system on 35K historical Jira tickets and 2 years of bug data, reducing manual RF mapping effort for triage engineers across 400+ RF clusters. Engineered a FAISS-based semantic retrieval pipeline that surfaces the Top-5 RF candidates from bug summaries and descriptions, reaching 80% prediction accuracy across highly overlapping RF labels. Integrated Claude Opus for RF classification with confidence scoring and evidence-backed reasoning, cutting duplicate RF creation and improving Mean Time to Resolution (MTTR).
Cultural Fit Analysis
The candidate has experience across diverse projects (Uber Eats cohort analysis, AI-powered root failure classification) and companies (Uber, Google, ThinkMates), indicating adaptability. The role as a Data Analyst aligns well with a Data Engineer target role, showing a progression towards more complex data infrastructure challenges. The breadth of skills including SQL, Python, LLM evaluation, and Power BI suggests a versatile individual who can contribute to various aspects of data initiatives.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through incident categorization and root cause analysis. Their experience in automating reports and validating data suggests an operational mindset focused on efficiency and accuracy. The mentoring experience indicates leadership potential and a collaborative attitude.