Senior Data Scientist – Retail Analytics & Data Products

About the role

You will work at the core of a Retail Digital platform, transforming large-scale POS data into real, production-ready data products.

This is not a research role. Your work will directly power business decisions, from promotion effectiveness and trader insights to recommendations and sales forecasting.

What you will do

You will take ownership of data and analytics capabilities across the platform, working with transactional data such as sales, promotions, and trader behavior

You will:

• turn raw data into clean, business-ready datasets
• build and deploy machine learning models used in production
• create scalable data solutions for analytics, dashboards, and AI use cases
• collaborate closely with engineering teams to ensure everything runs reliably in production

You will work across the full lifecycle, from data exploration and feature engineering to deployment, monitoring, and continuous improvement.

How you work

This role sits at the intersection of data science, engineering, and product. You won’t work in isolation, your solutions need to scale, integrate, and deliver real value. As a senior member of the team, you will also guide data modeling decisions, shape use cases, and help define best practices.

Impact

Your work will directly help traders make better decisions, improve customer loyalty, and enable new AI-driven capabilities across one the biggest retail ecosystem

Must-Have Requirements

• 6+ years of experience as a Data Scientist (or similar), delivering data science solutions in production
• Strong experience in machine learning, advanced analytics, and data modeling applied to real business problems
• End-to-end ownership of the data science lifecycle (data exploration → modeling → deployment → monitoring)
• Solid SQL skills and experience working with large-scale datasets
• Experience working in cloud environments (preferably GCP or similar)
• Good understanding of MLOps practices and working with engineering teams to deploy and operate models
• Strong communication skills, with the ability to translate data insights into business value
• Ability to work independently, take ownership, and operate in cross-functional teams
• Fluent English

Tech Stack:

• Programming: Python
• ML Libraries: scikit-learn, TensorFlow, PyTorch, Polars (or similar)
• Data Processing & Storage: SQL, BigQuery (or similar data warehouses)
• Cloud: Google Cloud Platform
• ML Platform: Vertex AI
• MLOps / Infrastructure: containerization & orchestration tools