London, England, United Kingdom
I'm a Senior Data Scientist with a strong foundation in Industrial Engineering and a track record across high-impact roles at McKinsey & Company, Infarm, and Getir, I thrive at the intersection of strategy, data, and innovation. Over the past few years, I’ve delivered scalable machine learning solutions, optimization models, and decision intelligence tools across industries—from real estate and e-commerce to retail and food tech. My experience spans everything from database migration and reporting standardization to pricing optimization, geospatial analytics, and predictive modeling. Whether leading cross-functional teams, mentoring junior colleagues, or collaborating with senior stakeholders, I bring both technical depth (Python, SQL, PySpark, Tableau, Power BI) and strategic thinking to the table. My work has helped clients realize multimillion-dollar gains, streamline operations, and unlock new growth opportunities. Passionate about turning data into action, I’m always eager to push boundaries—whether that means building a custom decision tree model to explore M&A opportunities, or deploying a routing algorithm to optimize warehouse operations. I’m also a consistent top performer in AI hackathons, with 10+ awards from organizations like ING, SAP, and Turkish Airlines.
Senior Data Scientist building and deploying production AI systems in client environments. Work spans LLM applications, agentic workflows, and full-stack data platforms, owning everything from problem scoping and system design to implementation and rollout. Strong focus on turning AI capabilities into reliable, user-facing products. - Created ETL processes, worked on Data governance definitions of European entities for a global real estate client, to build a European property performance dashboards, led the efforts for half of the dashboards, client workshops, working with data engineers etc. - Implemented the pricing tool to an e-commerce company, created reporting ETL processes using Databricks, developed a newer tool as per clients need, then, led client’s outsourced data scientist team of 6 people, to do newer developments in the tool and the pipelines. - Created footprint analysis and explanatory ML model for an early childcare company in the US, to understand the whitespace and M&A opportunities, visualized using Tableau. Also, footprint analysis using POIs around the Middle East, Europe. - Created a real-estate negotiation tool for a footwear company, created processes from different data providers, that creates a tool for stakeholders to use and negotiate their rent terms with property managers. - Value creation, due diligence, M&A, whitespace analysis
Reporting standardization , developed centralized 5 crop performance dashboards for each country’s account managers. Data migration effort from Zoho CRM to Salesforce
Campaign optimization and targeted client reach : Built end-to-end ML project for better customer reach, better campaign creation. Pricing optimization using ML and Bayesian models. Shelf optimization using routing model and mathematical model. Product analytics: Created customer journey model using qpp data of the customers
A machine learning project based on building a fashion recommendation engine from scratch was the objective of the internship. Several deep learning models were used in the course of the project which classify users' images and return most similar items through hundreds of fashion images and labels from LC Waikiki's e-commerce site. A successful working prototype was built in one month. Responsibilities: Data cleaning and manipulation of thousands images and labels. Building a custom batch processing pipeline for scaling deep learning models. Deployment of deep learning models to a Flask application as a minimum valuable product. Preparation of technical reports and presentations every week and final technical research paper.