Staff Data Scientist (AI & ML)

Overview

Responsibilities

• Leveraging ambiguous business problems as opportunities to drive objective criteria using data.
• Solving complex business problems using the simplest, most appropriate algorithms to deliver business value.
• Designing and implementing effective and impactful machine learning and generative AI systems in production.
• Developing a deep understanding of the product experiences and business processes that make up your area of focus.
• Developing a deep familiarity with the source data and its generating systems through documentation, interacting with the engineering teams, and systematic data profiling.
• Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus.
• Working closely with product and business teams to identify important questions that can be answered effectively with data.
• Delivering well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision-making through deep analysis and automated reports.
• Designing, planning, and analyzing experiments (A/B and multivariate tests).
• Supporting product and business managers with KPI design and goal setting.
• Mentoring other data scientists in their growth journeys.
• Contributing to improving our ways of working, our tooling, and our internal training programs.

Technical Experience

• Experience in machine learning, generative AI, deep learning, recommendation systems, pattern recognition, data mining, and artificial intelligence.
• Deep knowledge and experience in ML and GenAI frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch, Hugging Face Transformers, LLM fine-tuning).
• Excellent SQL.
• Competence with reproducible data analysis using Python or R.
• Familiarity with data modeling and dimensional design.
• Strong command over the entire data lifecycle, including problem formulation, data auditing, rigorous analysis, interpretation, recommendations, and presentation.
• Familiarity with different types of analysis, including descriptive, exploratory, inferential, causal, and predictive analysis.
• Deep understanding of various experiment design and analysis workflows and the corresponding statistical techniques.
• Familiarity with product data (impressions, events, etc.) and product health measurement (conversion, engagement, retention, etc.).
• Experience with LLMs and NLP