Data Scientist (all genders)

Responsibilities

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

Basic qualifications

• University degree (preferably Master's) 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 (complex joins, window functions), and core data science libraries (Pandas, Scikit-learn, Matplotlib, XGBoost)
• Proficiency with Databricks or similar lakehouse/notebook platforms
• Familiarity with BI tools such as Power BI or Tableau
• Ability to select and justify modeling approaches based on data characteristics, business constraints, and interpretability requirements
• Understanding of model performance trade-offs (e.g., precision vs. recall) 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 (prompt engineering, evaluation, orchestration)
• At least 2–3 years of experience with MLOps practices and experiment tracking (e.g., MLflow)
• Experience working with at least one cloud platform (AWS, GCP, or Azure)
• Strong ability to communicate complex findings to non-technical stakeholders
• Strong analytical thinking with a commercial mindset — able to start from a blank canvas and create measurable value
• Curiosity to dig into unfamiliar business domains and ask "why" before jumping to modeling
• A bias toward pragmatic solutions — shipping an 80% model fast over perfecting one in isolation
• Comfort navigating ambiguity — translating vague business problems into structured analytical approaches
• Ownership of data quality — you question the data before trusting the output
• Ability to manage competing stakeholder priorities and align on what's worth solving
• Self-direction — you don't wait for a ticket to identify and pursue high-impact work
• Willingness to challenge assumptions, including your own
• Working knowledge of Kubernetes and Docker
• Experience with APIs (web services)
• Familiarity with stream processing frameworks (e.g., Kafka)
• Experience designing and analyzing A/B tests
• Knowledge of version control (Git)
• Experience building end to end AI applications

About the company