Lead Data Scientist

Who we are

About this role

What you’ll do

• Frame customer, commercial and operational challenges as testable Data Science questions, defining outcomes, baselines, assumptions, decision thresholds and measures of value.
• Lead the development and validation of predictive, forecasting, propensity, anomaly and fraud, optimisation and causal models for prioritised use cases.
• Define appropriate experimental design, sampling approaches, feature strategies, evaluation methods and statistical tests based on the decision being supported and the available data.
• Lead modelling, experimentation and scientific validation, ensuring analytical approaches are proportionate to the problem and evidence available.
• Set and assure scientific standards for reproducibility, peer review, explainability, fairness, bias, uncertainty and model limitations.
• Evaluate model validity and recommend whether models should proceed, be redesigned or stop where sufficient validity or value cannot be established.
• Partner with Product, Data Engineering and ML & AI Engineering to ensure validated models are actionable and suitable for productionisation.
• Measure post-launch performance and real-world customer or business impact to establish whether deployed Data Science solutions are achieving their intended outcomes.
• Provide scientific input into data, tooling and experimentation choices.
• Support effective transition of scientifically validated models into reliable production environments while maintaining clear accountability between Data Science and ML/AI Engineering.

Who you are

• Extensive applied Data Science experience, including leadership of complex modelling and experimentation.
• Advanced Python capability and experience with relevant statistical and machine learning libraries.
• Advanced understanding of supervised and unsupervised machine learning, forecasting, causal inference and experimental methods.
• Experience selecting evaluation approaches, statistical tests, decision thresholds and validation techniques appropriate to different analytical problems.
• Strong understanding of model evaluation, calibration, explainability, fairness, bias, uncertainty and reproducibility.
• The scientific judgement to select proportionate approaches, communicate uncertainty clearly and recommend stopping or redesigning work where validity or measurable value cannot be demonstrated.
• Experience with cloud analytics and machine learning environments, preferably BigQuery and Vertex AI, or equivalent technologies.
• Strong working knowledge of production ML constraints and experience collaborating effectively with Data Engineers and ML & AI Engineers.
• The ability to convert customer, commercial and operational requirements into measurable analytical outcomes.
• Experience assessing deployed models against real-world customer or business outcomes.
• A postgraduate qualification in a quantitative discipline or equivalent depth of applied experience.

Not a perfect fit?

What's in it for you

• The opportunity to provide senior scientific leadership across Data Science and applied-model development.
• Work on customer, commercial and operational use cases spanning predictive modelling, forecasting, propensity, anomaly and fraud detection, optimisation and causal modelling.
• Influence whether Data Science use cases progress into production based on scientific validity, measurable value and real-world outcomes.
• Collaborate across Product, Data Engineering and ML & AI Engineering, helping connect scientific development with production-ready solutions.
• Shape scientific standards for responsible modelling, including reproducibility, explainability, fairness, bias and uncertainty.
• Work with cloud-based analytics and ML capabilities, including BigQuery and Vertex AI or equivalent technologies.
• Contribute to Data Science solutions designed to deliver measurable customer and business impact.

What skills you will learn

• Applied scientific leadership across complex Data Science and AI use cases.
• Advanced experimentation and causal inference, including experimental design, sampling, statistical evaluation and impact measurement.
• Responsible AI and model governance, with a focus on explainability, fairness, bias, uncertainty and reproducibility.
• Cloud-based Data Science and ML practices, including exposure to BigQuery and Vertex AI or equivalent environments.
• Production ML collaboration, working across Data Engineering and ML & AI Engineering to transition validated models into production.
• Post-launch model evaluation, connecting model performance with measurable customer and commercial outcomes.
• Business translation and scientific judgement, strengthening the ability to turn complex operational or commercial questions into testable hypotheses and evidence-based decisions.

VOIS Equal Opportunity Employer Commitment

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