Lead Data Scientist

Roles and 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 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 work, our tooling, and our internal training programs.

Minimum Requirements

• Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.
• 3+ years of overall experience working in data science or machine learning.
• Experience doing data science in an online consumer product setting is a plus.
• A good problem solver with a ‘figure it out’ growth mindset.
• An excellent collaborator.
• An excellent communicator.
• A strong sense of ownership and accountability.

Desired Candidate Profile

• Experience in machine learning, deep learning, recommendation systems, pattern recognition, data mining, and artificial intelligence.
• Deep knowledge and experience in ML algorithms and frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch ...).
• 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 the 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.).
• Familiarity with BigQuery and the Google Cloud Platform is a plus.
• Data engineering and data pipeline development experience (e.g. via Airflow) is a plus.