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Data Analyst with 3+ years in Data Analytics & Machine Learning
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Data Analyst with a Master's in Data Analytics and Decision Science from RWTH Aachen University and hands-on experience in enterprise data analytics at Ford. Skilled in Python, SQL, and Power BI for building data pipelines, dashboards, and business insights. Experienced in data quality, KPI development, and cross-functional analytics. Strong interest in applying data to solve business and operational problems.
RWTH Aachen University
Masters · Data Analytics and Decision Science
August 1, 2023 – June 30, 2026
Reva University
Bachelor of Technology · Electrical and Electronics Engineering
August 1, 2016 – June 30, 2020
Ford Motor Company - Global Data Insights & Analytics
Data Engineering Intern
August 1, 2024 – March 1, 2025
Köln, North Rhine-Westphalia, Germany
Tata Consultancy Services – Commonwealth Bank of Australia
Software Test Automation Engineer
November 1, 2021 – September 1, 2023
Bengaluru, Karnataka, India
Tata Consultancy Services – Bankwest Australia
Junior Application Developer
January 1, 2021 – November 1, 2021
Bengaluru, Karnataka, India
Climate Change & Energy Consumption Analysis
June 1, 2026 – June 1, 2026
Integrated multi-source datasets to analyze relationships between temperature, energy demand, and CO2 emissions across countries. Built KPI-driven insights on electricity demand sensitivity to temperature and quantified macro trends in energy consumption growth and fossil-fuel dependence. Visualized trends in renewable adoption and carbon intensity to support data-driven insights.
Identifying Cognitive Bias in Supply Chain Using Data Analytics
June 1, 2026 – June 1, 2026
Designed and ran a supplier-selection discrete choice experiment to test cognitive bias in multi-criteria procurement decisions. Analyzed survey responses using logit-based discrete choice modeling and exploratory analytics to quantify how "industry-preferred" framing shifts selection behavior and decision confidence.
Quantifying Demand Flexibility and Price Sensitivity from Smart Meter Data Using Machine Learning
June 1, 2026 – June 1, 2026
Analyzed large-scale smart meter data to identify electricity demand patterns across multiple consumer sectors. Built predictive models using regression and XGBoost to quantify price sensitivity and demand behavior. Applied SHAP-based interpretability to explain non-linear drivers of energy consumption. Developed forecasting models to assess demand predictability and flexibility across sectors. Created a composite Energy Vulnerability Index to support data-driven policy and decision-making.
Building Energy Performance Forecasting (ML Classification)
June 1, 2026 – June 1, 2026
Developed machine learning models to classify residential energy efficiency using heating and cooling load data. Optimized model generalization by conducting rigorous Exploratory Data Analysis (EDA) and implementing feature selection techniques. Refined model accuracy through K-fold cross-validation and hyperparameter optimization ensuring high performance across unseen data. Evaluated model performance using metrics such as F1-score and ROC-AUC, ensuring robustness across unseen data.
Transport Optimization in DHL Parcel Network
June 1, 2026 – June 1, 2026
Developed a vehicle routing model to optimize parcel flows across 37 logistics hubs under time and capacity constraints. Evaluated routing strategies based on cost, distance, and on-time delivery performance. Incorporated operational constraints such as sorting capacity and buffering to simulate real-world conditions.
AWS Cloud Practitioner
Unknown
June 1, 2026 – Present
Power BI
LinkedIn learning
June 1, 2026 – Present
Artificial Intelligence Foundations
LinkedIn learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background from RWTH Aachen University and experience at Ford Motor Company, Tata Consultancy Services, and various academic projects demonstrate adaptability and exposure to diverse work environments. The focus on data-driven policy and decision-making, energy consumption, and supply chain optimization aligns with roles requiring analytical rigor and impact. The breadth of skills from data engineering to machine learning and visualization indicates a versatile profile suitable for dynamic data teams.
Soft Skills & Operational Fit
The candidate's project descriptions and work experience indicate a strong analytical mindset, problem-solving capabilities, and a structured approach to data analysis. Collaboration with developers and product managers in previous roles suggests good teamwork and communication skills. The academic projects demonstrate initiative and the ability to apply advanced analytical techniques to real-world problems.