Staff Data Scientist / Tech Lead

About the Project:

Staff/Tech Lead role driving AI decisioning at scale. Systems have strict real-time constraints (<40 ms) and aim to improve conversion (~5%). Define roadmap, challenge assumptions and ensure reliable ML in production.

Responsibilities:

• Design and implement production ML systems (real-time decisioning, personalization);
• Drive contextual bandits, uplift modeling, advanced recommendation frameworks;
• Define experimentation (A/B testing, causal inference, synthetic controls);
• Oversee feature engineering at scale, model monitoring and validation;
• Own decisioning platforms and Martech integrations;
• Influence senior stakeholders on data science strategy;
• Mentor data scientists;
• Lead GenAI and LLM initiatives: fine-tuning, evaluation, content personalization.

Recruitment Process:

• Initial interview with recruitment;
• Hiring manager interview;
• Live coding assessment;
• Meeting with Project Manager.

Qualifications:

• 10+ years of experience in Data Science, with a strong track record in production ML systems;
• Expert-level Python (NumPy, Pandas, Scikit-learn) and SQL;
• Deep experience with PySpark / Apache Spark for large-scale data processing;
• Mastery of gradient boosting methods (XGBoost, LightGBM), recommendation systems, NLP, clustering, and uplift modeling;
• Extensive experience with A/B testing, causal inference, statistical modeling, and synthetic controls;
• Proven ability to build and maintain production ML pipelines on cloud platforms (AWS preferred);
• Experience with decisioning platforms, personalization engines, and Martech integrations;
• Hands-on experience with LLMs, prompt engineering, evaluation, and fine-tuning;
• Strong stakeholder management skills with the ability to challenge assumptions and ask the right questions;
• Databricks experience is a nice to have.