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Data Science with 1+ years in data analysis & machine learning
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Assessing your cultural and operational fit
Associate Data Analyst / Analytics Professional with hands-on experience in SQL, Python, Excel, and BI tools, working on real-world datasets across analytics, reporting, and machine learning projects. Skilled in data cleaning, EDA, KPI monitoring, dashboard creation, and anomaly detection. Experienced in collaborating with cross-functional teams, documenting analysis, and translating business questions into actionable insights in fast-paced environments.
Dr. Babasaheb Ambedkar Marathwada University
Bachelor of Science
June 1, 2022 – July 1, 2025
Innodata Inc.
Data Annotator / Analytics & AI Content Reviewer
November 1, 2025 – Present
India
MentorMind | Uber × upGrad
Data Science Intern
July 1, 2025 – December 1, 2025
India
Commodity Price Analysis
June 16, 2026 – Present
Designed and optimized a relational database integrating commodity, region, and price data. Executed complex SQL queries, including joins, aggregations, and window functions to analyze real-world data. Evaluated price variations and regional trends across India (2019–2021) to identify cost-sensitive commodities. Produced insights on top commodities, state-wise demand patterns, and price fluctuations to guide strategic decisions. Used SQL queries to answer business-style analytical questions and perform trend comparisons.
View ProjectWeather Analytics Power BI Dashboard
June 16, 2026 – Present
Designed an end-to-end Power BI dashboard integrating real-time and forecast weather data using Weather API. Built a star-schema data model (current, hourly, daily facts with location dimension) for scalable analytics. Cleaned, normalized, and transformed raw API data using Power Query (M) to create analytics-ready datasets. Developed DAX measures for temperature trends, AQI categorization, and pollution metrics (PM2.5, PM10, CO, NO2). Created interactive visualizations with dynamic city-level filtering and automated data refresh for near real-time insights.
TripPulse - Ride Delay Prediction & Data Analysis
June 16, 2026 – Present
Performed end-to-end data science analysis on 1500+ ride records, including EDA and feature engineering. Engineered time-based and route-level features to capture real-world patterns. Built and trained a Random Forest model achieving 89% accuracy. Interpreted feature importance and visualized key delay drivers. Monitored model performance metrics and validated outputs to detect anomalies and ensure consistency across data splits.
View ProjectSQL (Basic)
HackerRank
May 1, 2025 – Present
Business Analysis Foundations
Microsoft & LinkedIn Learning
April 1, 2025 – Present
Data Analytics
Deloitte (Forage)
March 1, 2025 – Present
Certified in Data Science & Artificial Intelligence (AI/ML)
UpGrad
January 1, 2025 – Present
The candidate scored 94% on the 'Data Scientist — Artificial Intelligence' test, indicating a very strong grasp of the core concepts and practical applications in data science and AI/ML. This high score reflects proficiency in key areas such as model building, feature engineering, and data analysis.
Strengths
Cultural Fit Analysis
The candidate's academic projects and certifications demonstrate a proactive learning attitude and a broad interest in various aspects of data science and analytics, including data engineering, visualization, and machine learning. The freelance and internship experiences show adaptability to different work environments and a commitment to continuous skill development. The diversity of projects (ride prediction, commodity analysis, weather dashboard) indicates a versatile approach to problem-solving and a willingness to tackle different data domains, aligning well with a dynamic team culture.
Soft Skills & Operational Fit
The candidate's experience as a Data Annotator/Analytics & AI Content Reviewer highlights attention to detail, analytical judgment, and adherence to compliance guidelines. The project descriptions indicate an ability to document findings and translate business questions into actionable insights, suggesting good communication and problem-solving skills. The academic projects demonstrate initiative and the ability to work independently on complex data challenges.
Limitations