Lead Data Scientist

Design, build, and productionize AI solutions, especially LLM and generative AI workloads, on top of our Enterprise data platform (Databricks, Azure, OpenAI, and AWS). This role has an emphasis on the operationalization of AI solutions, ensuring AI products are reliable, scalable, secure, and cost-effective. Effective in production. You will partner with data engineering, data science, and product teams to turn ideas into robust, continuously running services.

Responsibilities:

• Lead high-impact analytical and modelling 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, 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 modelling, and diagnostics.
• 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 modelling, 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 and inspiring team members with consistent focus, quality output, and timelines.
• Constantly keep oneself updated on current technology & 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.

Skills

ML, PySpark, Python, causal inference, experimental design, machine learning, probability, regression, scikit-learn, statistics