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Data Scientist with 10+ years in Data Analysis & Machine Learning
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Experienced professional with a strong background in engineering education, successfully transitioned to Data Science through intensive self-directed learning and project-based experience. Proficient in Python, SQL, Tableau, and Machine Learning techniques. Demonstrated ability to analyze complex datasets, extract actionable insights, and build predictive models to support business strategy and optimization across various domains including logistics, e-commerce, and ride-sharing.
Scaler
Data Science and Machine Learning
August 1, 2025 – Present
Syed Ammal Engineering College
BE · E.C.E
N/A – June 30, 2009
Sathak Engineering College
Lecturer
March 1, 2010 – September 1, 2012
India
Delhivery Sales Analysis
June 1, 2026 – Present
Analyzed 1.4L+ logistics trips to evaluate delivery performance, which involved cleaning and imputing missing values, converting 3+ timestamp fields, and engineering segment-level features. Identified delays using actual vs OSRM time/distance and FTL routes showed 2× higher delivery time than Carting. Delivered insights on peak trip hours, high-volume hubs, and route inefficiencies to support logistics optimization.
View ProjectTarget Brazil Data Analysis
June 1, 2026 – Present
Analyzed 100K+ Brazilian e-commerce orders using advanced SQL to uncover trends in seasonality, geography, and customer behavior. Pinpointed top-performing states (SP, RJ) and key delivery bottlenecks by analyzing time-to-deliver and freight costs, supporting a business strategy that saw a 390% YoY growth.'. Delivered actionable insights on payment types, customer distribution, and logistics optimization to support business strategy.
View ProjectYulu Bikes Demand Analysis
June 1, 2026 – Present
Analyzed 10K+ rows of Yulu bike rental data to identify demand patterns across season, weather, and working days. Performed statistical tests (T-test, ANOVA, Kruskal-Wallis) to validate variable impact on rental count. Detected skewness, outliers, and correlations using IQR, Shapiro-Wilk, and heatmaps; retained outliers to preserve insights.
View ProjectOla Driver Attrition Prediction Case Study
June 1, 2026 – Present
Transformed 19K+ monthly Ola driver records into a clean, driver-level dataset using pandas, datetime parsing, and KNN imputation for age/gender gaps. Engineered churn-relevant features like income growth, rating improvement, and reporting frequency; derived binary attrition target from LastWorkingDate. Aggregated and reshaped data to 2.3K unique drivers with 16 features, enabling churn modeling with LightGBM, XGBoost, and Random Forest. Enabled churn prediction for retention strategy across cities, grades, and designations—supporting cost-effective driver engagement
View ProjectElectric Vehicle Data Analysis
June 1, 2026 – Present
BEVs account for 80.2% (104,850 vehicles), while PHEVs make up 19.8% (25,966 vehicles). Tesla dominates with 52.7% share, led by Model Y (21.8%) and Model 3 (21.2%). The average electric range is 73.1 miles, showing steady improvement in EV performance. EV adoption has grown consistently from 2011–2024, with a strong surge after 2020.
View ProjectCultural Fit Analysis
The candidate shows a strong drive for career transition into Data Science, evidenced by intensive self-directed learning and multiple personal projects. The project diversity (logistics, e-commerce, demand prediction, attrition) indicates a broad interest in applying data science across different domains. The target role 'Data Scientist' aligns well with the skills and projects showcased. The educational background in E.C.E. combined with a recent Data Science and Machine Learning program from Scaler suggests a commitment to upskilling and adapting to new fields.
Soft Skills & Operational Fit
The candidate's background as a lecturer suggests strong communication and structured problem-solving abilities, which are valuable for explaining complex data concepts and collaborating within a team. The self-directed learning in data science indicates a proactive and adaptable work attitude. However, the resume lacks explicit details on teamwork in data science projects or handling operational challenges in a data science role.