What youll do

• Design, develop, and deploy machine learning and AI solutions that solve commercial problems across pricing, demand, and revenue optimization
• Own model quality end-to-end — monitor performance, improve accuracy, ensure explainability, and quantify business impact throughout the model lifecycle
• Collaborate with Business Analysts, Data Engineers, Revenue Management, and Subject Matter Experts to deliver production-grade data products
• Identify opportunities where emerging techniques (GenAI, advanced forecasting, optimization) can create measurable business value — and build them

Our Tech Stack: PySpark, Python, Databricks, Azure Cloud, MLflow

What youll need

• University degree (preferably Masters) in Economics, Computer Science, or a STEM-related field
• 4–5 years of professional experience as a Data Scientist or ML Engineer
• Solid understanding of Python, SQL and core data science libraries
• Familiarity with BI tools

• Proficiency with Databricks or similar lakehouse/notebook platforms
• Ability to select and justify modeling approaches based on data characteristics, business constraints and interpretability requirements
• Understanding of model performance trade-offs and hyperparameter tuning
• Experience with feature engineering, data cleaning and managing data leakage and overfitting
• Experience with constrained optimization models
• Experience building RAG pipelines or integrating LLMs into production workflows
• At least 2–3 years of experience with MLOps practices and experiment tracking
• Experience working with at least one cloud platform

What youll bring

• Curiosity to dig into unfamiliar business domains and ask "why"
• A bias toward pragmatic solutions
• Comfort navigating ambiguity
• Ownership of data quality