Data Scientist in Transition | 12 years Data Engineering | Python | SQL | NLP | MSc BigData Analytics
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Assessing your cultural and operational fit
Resume-Parser
May 5, 2026 – Present
Text-based resume parser in R & R Shiny — auto-extracts Job Titles, Skills and Companies using custom-built dictionaries
View ProjectDonor-Reactivation-Prediction
May 5, 2026 – Present
Classifies lapsed donors likely to contribute ≥ €30 using R — 45+ engineered features, 6 models evaluated with AUC, ROC & lift curves
View ProjectLeroy-Merlin-Product-Recommendation
May 5, 2026 – Present
Predicts top front-display products to maximise sales uplift using R — integrating 10 data sources, predictive modelling & R Shiny dashboard
View ProjecteCommerce-Customer-Behaviour-Analysis
May 5, 2026 – Present
Customer segmentation using KMeans clustering and PCA on eCommerce shopping behaviour survey data — 800 responses, 24 features, Python
View ProjectCars24-Used-Car-Price-Prediction
May 5, 2026 – Present
End-to-end ML project — web scraping Cars24 with Playwright across 15 cities, feature engineering and Random Forest price prediction (Test R2: 0.81)
View ProjectDivvy-Bikeshare-Rider-Analysis
May 5, 2026 – Present
Year-on-Year EDA of Divvy Chicago bike-share data (2019 vs 2020) — rider behaviour, commute patterns and COVID-19 impact using Python
View ProjectZomato-Bangalore-Restaurant-Analysis
May 5, 2026 – Present
EDA on 51,717 Zomato Bangalore restaurants — ratings, cost, location, cuisine and nightlife analysis using Python
View ProjectNLP-Fashion-Trends-Extraction
May 5, 2026 – Present
NLP pipeline to extract and classify latest fashion trends from text data — built during internship at GAPRO, Paris (2017)
View ProjectSeattle-Airbnb-Price-Prediction
December 8, 2020 – December 10, 2020
Python code to predict the booking price of a new property in Airbnb Seattle
View ProjectData-Science-Playground
August 21, 2019 – Present
Experimental ML and data science projects covering classification, regression, clustering and NLP — Python & R
View ProjectCultural Fit Analysis
The candidate's project portfolio is diverse, covering NLP, predictive modeling, customer segmentation, and EDA across various domains (fashion, e-commerce, real estate, transportation). This breadth suggests adaptability and a willingness to explore different problem spaces, which can be a positive for cultural fit in a dynamic environment. However, the lack of team-based projects or professional experience makes it difficult to assess collaboration and alignment with organizational values. The experience level is 0, which might indicate a need for mentorship and integration into a team culture.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven approach to learning and applying data science concepts. The variety of projects suggests an ability to tackle diverse problems. However, without psychometric test results or interview data, it is difficult to assess specific soft skills like teamwork, stress handling, or communication clarity in a professional setting.