Senior Data Scientist - Pricing & Machine Learning

Senior Data Scientist - Pricing & Machine Learning

Location: Leeds, UK (hybrid)

Contract: Permanent, full-time

Salary: Competitive

About Protect Group

Protect Group helps businesses improve the customer experience and generate additional revenue through innovative technology. Since 2016, we've grown to support more than 400 partners across 75+ countries, with 12 offices worldwide.

Our AI-driven technology integrates with online booking and sales platforms, helping businesses across travel, transport, hospitality, events and financial services offer more flexible, customer-friendly experiences.

We're an ambitious, collaborative team that values accountability, fresh thinking and people who take ownership. We move quickly, learn from evidence and work together to solve meaningful problems.

The role

Pricing machine learning sits at the heart of our business. Our pricing engine dynamically sets protection rates across hundreds of partners and millions of transactions, directly influencing revenue and conversion for our partners.

We're looking for a hands-on Senior Data Scientist to take a leading role in developing and improving this capability. Reporting to the Head of Data Science, your primary focus will be pricing optimisation: designing models, running experiments and turning commercial opportunities into reliable production systems.

You'll also contribute to our wider machine learning work, including risk modelling and agentic AI systems for refund decision-making and automation.

This is a role for someone who enjoys both developing sophisticated models and putting them into production. You'll help shape our technical direction while remaining close to the code and accountable for what we ship.

You'll join a flat, high-trust team of data scientists and engineers, working closely with commercial, finance and product teams, as well as the wider technical teams behind our AI platform.

What you'll work on

• Pricing optimisation: Developing and improving dynamic pricing models across partners, products and markets.
• Experimentation: Designing backtests, online experiments and always-on optimisation approaches to measure the effect of pricing changes.
• Commercial modelling: Balancing revenue, conversion and customer value within pricing decisions.
• Risk models: Building models that improve our understanding of claims, refunds and transaction-level risk.
• Agentic AI systems: Creating graph-based, multi-step workflows using loops, branching, routing, tool use, state management, retries and human-in-the-loop controls.
• Evaluation: Using regression sweeps, canary testing and LLM-as-judge scoring to determine what is ready to ship.
• ML infrastructure: Building versioned training pipelines, model registries, scheduled jobs and deployments using Azure ML.

What you'll be responsible for

• Owning the development and continuous improvement of our production pricing models.
• Turning pricing opportunities into measurable hypotheses, experiments and production changes.
• Evaluating pricing performance through backtesting, online testing and commercial metrics.
• Developing Bayesian, multi-armed bandit and other optimisation approaches for dynamic pricing.
• Working with commercial, finance and product teams to understand pricing performance and recommend action.
• Building supporting risk models and agentic systems where they improve pricing, refund decision-making or operational efficiency.
• Applying rigorous offline and online evaluation to everything you build.
• Shipping reliable models through reproducible training pipelines, versioned artefacts, scheduled jobs, monitoring and alerting.
• Raising the team’s technical standards through code and model reviews, mentoring and the development of reusable patterns.
• Helping the Head of Data Science shape the team's strategy and identify where investment in machine learning will have the greatest impact.
• Communicating findings, trade-offs and recommendations clearly to both technical and non-technical audiences.

What you'll bring

• At least five years' experience in data science or machine learning, including responsibility for models running in production.
• Experience of insurance, protection, travel, fintech, risk modelling or another transaction-led industry.
• Strong hands-on experience building pricing, revenue optimisation or commercially focused decision models.
• Expert Python skills, including production-quality code, testing and code review, alongside strong SQL.
• A solid grounding in machine learning techniques such as gradient boosting, GLMs, Bayesian methods and multi-armed bandits, or comparable experimentation and optimisation methods.
• Experience designing, running and evaluating pricing experiments using both commercial and statistical measures.
• The ability to connect model performance with commercial outcomes such as revenue, conversion and risk.
• Hands-on experience designing, building and optimising agentic systems, particularly graph-based and iterative workflows involving loops, branching, routing, tool use and state management.
• Experience evaluating LLM applications using approaches such as gold datasets, regression testing, LLM-as-judge scoring and canary testing.
• Experience with cloud ML platforms - ideally Azure ML, Functions and Blob Storage or the willingness to transfer your knowledge to Azure.
• Experience with Git-based workflows, CI/CD and modern agent-assisted development tools such as Claude Code or Codex.
• The ability to turn an ambiguous business problem into a deployed, monitored solution.
• Strong commercial judgement and confidence discussing model decisions in terms of revenue and risk.
• Clear written and verbal communication, including with non-technical audiences.
• An evidence-led approach and experience mentoring others or providing technical leadership.
• Awareness of GDPR and international data regulations.

Useful, but not essential

• Causal inference and large-scale experiment design.
• MLOps, including model registries, artefact versioning and drift monitoring.
• Streamlit or similar tools for internal applications and dashboards.
• Experience with claims, refunds or customer operations.

You don't need to match every item in this section. If the role sounds like a strong fit, we'd still like to hear from you.

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