Senior Data Scientist, Decision Science

About the role

We already run two kinds of agent alongside our teams. AI agents diagnose performance problems and tell people the root cause. Rule-based agents recommend marketing actions to our advertising teams. The quality of their decisions shows up directly in our revenue.

Next, both take a greater role in the day-to-day running of our lead generation business. Every agent needs predictions and decisions, so we need someone who brings both skill sets. Classical ML to predict lead quality and traffic performance, explain drops and forecast. Decision science to learn which action works best when, and prove it beats the rules.

You'll join a small data science team and work day to day with our AI engineers, product managers and the operators who use the agents.

What we're offering you:

• Summer Fridays

• Competitive holiday benefits - 25 days a year paid holiday, plus 8 bank holidays (increases 1 day a year up to 30 days)

• Hybrid working - 3 days a week in the office

• Closed for Christmas holidays - Extra days not taken from your annual holiday allowance.

• Work from anywhere for 2 weeks a year

• Life Assurance and Income Protection to protect your loved ones

• Benefits allowance for health, dental, and vision coverage

• Six months paid maternity leave, and one month paid paternity leave (subject to qualifying conditions) inclusive of same-sex and adoptive parents

• Defined Contribution Pension and Salary Sacrifice Scheme

• Be Well: Our award-winning wellbeing and mental health programme to support all MVFers and their families

• Family Forward support for our MVF parents and their mini-mes

• 2 charity days a year

What you'll do

Classical ML

• Build models. Classification and ranking for lead quality and traffic performance, anomaly detection, and forecasting.

• Evaluate and monitor them. Sound validation offline, drift checks once live, working with our platform team, who run model deployment on SageMaker.

• Prove impact. Measure each model against commercial KPIs, with A/B tests where we can randomise.

Decision science

• Frame decisions. Turn a business rule into actions, context and reward, agreeing with operators what each decision optimises, with today's rules as the baseline.

• Learn policies. Contextual bandits with safe exploration, learning from operator overrides, evaluated off-policy before anything goes live.

• Set the evidence for hand-over. Measure how often agents agree with experts and how often they're overridden, and propose the confidence and guardrails an agent must clear before it acts alone. Point people in the business sign off; our AI engineers build the mechanism that enforces it.

What we're looking for

• 5+ years as a data scientist or in a similar applied ML role, with models you've taken to production and kept working.

• Statistics and experimentation: hypothesis testing, A/B testing, power and sample sizing.

• Classical ML: classification and ranking with scikit-learn and XGBoost or LightGBM, evaluated rigorously: validation design, calibration, leakage.

• Decision science: applied work with at least one of contextual bandits, causal inference or uplift modelling, and the depth to apply it independently.

• Tools: strong Python and SQL, Git and code review.

Nice to have

• Production experience with bandits, off-policy evaluation or uplift modelling (for example Vowpal Wabbit, Open Bandit Pipeline, EconML or DoWhy).

• Time-series forecasting for budgets, pacing or demand.

• Snowflake and dbt, AWS SageMaker.

• Lead generation or ad tech.

How you'll work

• Commercial impact. You understand how the business creates value and use that to make better choices about what gets built, prioritised or stopped. You define the intended impact before starting, consider the cost of the work and evaluate what happened afterwards.

• Delivery and ownership. You take responsibility for outcomes and move at a pace matched to the cost of delay. You follow work through until it is delivered, handed over or deliberately stopped, and remain accountable for what happens after delivery.

• Candour and communication. You surface important information early, including when it is uncomfortable, with evidence and a view on what to do about it. You communicate what is known, assumed and uncertain in a form the audience can understand and act on.

• Making others better. You create lasting improvement in other people's and the team's work through teaching, feedback or challenge, and take challenge on your own work as readily as you give it.

• Growth and mastery. You deepen your capability through deliberate practice, feedback and experience, build the skills the work will need next, and change established practices when better approaches become available.

• Judgement under ambiguity. You make sound decisions with incomplete information and know when to act, escalate or delegate. You own what you ship regardless of how it was produced, and change position when the evidence changes.

How you'll build

• Craft and AI-native delivery. You build robust analytical, modelling and AI solutions, choosing the right approach for the problem. You understand and can explain the evidence, assumptions, behaviour and limitations of what you build, and use AI to increase delivery speed without giving up technical ownership.

• Architecture and systems design. You design reliable systems that move modelling and AI capabilities from a business problem into effective use. You treat data and context, security and governance, monitoring, failure handling and recovery, cost, performance and lifecycle as part of the design rather than concerns added afterwards.