Data Analyst with 2+ years in Data Analytics & BI Tools
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Results-driven Data Analyst with 2+ years of experience delivering data-driven insights through advanced SQL, Python, and BI tools. Proven track record of building automated data pipelines that reduced manual processing time by 40% and optimizing complex queries for large-scale datasets. Expert in translating business requirements into actionable analytics solutions using statistical analysis and machine learning techniques.
MGM's College of Engineering
Bachelor of Technology · Information Technology
August 1, 2019 – June 30, 2023
Uptech Software Solutions Private Limited
Data Analyst
January 1, 2024 – Present
Hyderābād, Telangana, India
Outlier
AI Trainer (Part-Time)
January 1, 2023 – December 31, 2024
India
Ai Variant
Data Science Intern
January 1, 2023 – December 31, 2023
Bengaluru, Karnataka, India
Behavioral Analytics on Fitbit User Data
June 21, 2026 – Present
Analyzed user-level Fitbit data (steps, sleep, calories, mood) to uncover behavioral patterns and identify correlations between physical activity and emotional well-being to support personalized engagement strategies. Cleaned and transformed raw activity data using NumPy for optimized computation and analysis of large-scale time-series data. Mapped categorical activity status values (e.g., '500', 'o') to meaningful labels (‘Active', 'Inactive') to standardize behavioral segmentation. Computed aggregate metrics such as average and maximum step counts to identify peak activity trends and anomalies. Correlated mood patterns with activity levels, uncovering that users with >4000 steps were more likely to report positive moods. Segmented users based on step counts and mood states to enable targeted nudges for improving user engagement and retention.
Country-Level Development Insights Using McKinsey Dataset
June 21, 2026 – Present
Analyzed 1,700+ country-year records across 140+ countries and 5 continents to understand how GDP per capita correlates with life expectancy, aiding strategic decision-making in development economics and and public health policy. Conducted exploratory analysis on a structured dataset (1704 rows × 6 columns) containing metrics such as life_exp, year, country, and continent, covering ~55 years of global data. Identified variation in life expectancy across continents, revealing that regions like Africa showed lower average life expectancy (~50 years) compared to Europe (~75+ years). Applied multi-level sorting to detect chronological improvements and stagnation zones in public health indicators across countries. Engineered derived columns (e.g., growth deltas between life exp and year) to observe long-term progress patterns and generational health shifts. Cleaned and transformed categorical variables, resulting in 140+ unique countries and 5 major continents for accurate regional segmentation. Enabled policymakers to pinpoint high-risk regions, underperforming countries, and emerging success models, laying the foundation for targeted investment and impact forecasting.
Cultural Fit Analysis
The candidate's experience spans a full-time Data Analyst role, a part-time AI Trainer role, and a Data Science Intern role, indicating adaptability and a willingness to explore different facets of data. Their projects cover diverse domains like user behavior analytics and development economics, suggesting a broad interest in applying data skills to various challenges. This diversity aligns well with a dynamic work environment that values continuous learning and cross-domain application of skills.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project descriptions, where they identify problems, apply analytical techniques, and derive actionable insights. Their experience in collaborating with cross-functional teams for AI training datasets suggests good teamwork and communication. The focus on optimizing query performance and automating processes indicates an operational mindset geared towards efficiency.