Lead Data Scientist

Responsibilities:

• Lead high-impact analytical and modeling projects from problem definition to measurable business outcome.
• Translate ambiguous commercial questions into well-structured statistical or optimization problems.
• Design and run robust analyses, including hypothesis testing, experimental design (A/B and quasi-experimental), power calculations, and model validation and backtesting.
• Build predictive and prescriptive models using appropriate statistical and machine-learning methods.
• Critically assess model assumptions, bias/variance trade-offs, overfitting risks, and data limitations.
• Clearly communicate uncertainty, limitations, and trade-offs to non-technical stakeholders.
• Partner with engineering teams to ensure models are implemented reliably and responsibly in production.
• Contribute to our standards for validation, documentation, and reproducible analytical practice.
• Mentor the junior members of the team.

Requirements:

• 7+ years of software and data science experience.
• Strong foundations in statistics and probability, including hypothesis testing and confidence intervals, regression modeling and diagnostics, and experimental design and causal reasoning.
• Understanding of sampling, bias, variance, and uncertainty
• Demonstrable experience applying statistical or ML models to real business problems.
• Ability to explain: Why a model works, when it won't work, what assumptions it relies on, and how confident we should be in its outputs.
• Strong Python skills (e. g., pandas, scikit-learn, PySpark, or similar) and solid SQL capability.
• Experience validating models properly (cross-validation, holdouts, backtesting, sensitivity analysis).
• Clear and confident communication with both technical and non-technical stakeholders.
• A degree (or equivalent experience) in statistics, mathematics, computer science, engineering, or a related quantitative field.
• We value candidates who can reason deeply about a problem, not just apply a library.

Preferred Qualifications:

• Strong communication skills and stakeholder management skills.
• Innovative mindset and a go-getter attitude.
• Experience with optimization methods or operations research.
• Exposure to causal inference techniques (e. g., matching, uplift modeling, diff-in-diff).
• Experience applying NLP or GenAI in a statistically responsible way.
• Familiarity with model lifecycle tools (e. g., MLflow, Databricks).

Behavioral Competencies:

• Lead initiatives with high accountability and create a collaborative environment with a solution-based approach.
• Serve as a trusted advisor and mentor for the team offering technical advice.
• Ability to communicate with precision, engaging the team and stakeholders to build trust and reliable working relationships across cross-functional teams and geographies, enabling organizational alignment.
• Ability to set priorities with the team by understanding interdependence, inspiring team members with consistent focus, quality output, and timelines.
• Constantly keep oneself updated on current technology and trends to drive innovative decisions through piloting forward-thinking approaches to complex problems in one's own area of work.
• Inquisitive to understand customer priorities and business challenges while creating value for technical solutions.