Senior Applied Data Scientist / ML Engineer
- Compensation
- Not disclosed
- Location
- United Kingdom, Remote
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data science
- Posted
Skills
Nice to have
- Working With Agents: You've used coding agents on real modelling or engineering work, you can tell correct output from merely plausible output, and you invest in the scaffolding that makes the next mo
- Decision Intelligence: Experience with decision intelligence, forecasting, customer behaviour modelling, workforce/route optimisation, or operational intelligence products.
- Leadership: Prior experience as a senior or lead scientist or engineer responsible for guiding technical direction.
- LLM / Agentic AI: LLM or agentic workflows shipped into a product.
Full description
We are looking for a product-minded applied data scientist or engineer to turn raw operational data into products that measurably improve how our customers make decisions and run their businesses. Which label you carry matters less to us than whether customers end up better off.
This is not a research-only role, nor a service-oriented internal analytics position—and it is not a role for a model builder alone. We want an owner: someone who frames the problem, learns the domain, builds the data when it doesn't exist, ships the model, and stays with it until customers are acting on it and can measure the reward. The road runs through data engineering; deployment and testing are part of delivery, not a handoff. You understand that great models are not just accurate in notebooks—they are usable, explainable, and measurable inside a real product.
Whether you came to this work through statistics, software, analytics, or data engineering, you have a strong bias toward shipping.
What You'll Do
From Raw Data to Better Decisions
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Own the Outcome: Take an ambiguous customer problem, decide whether ML is even the right answer, build it, and stay with it until customers are acting on it.
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Learn the Domain: Get fluent in the semantics of how our customers operate—what a route, a crew, or a service history actually means. A model that is accurate but wrong about the domain creates nothing.
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Build the Data You Need: When the features don't exist, create them in Snowflake and dbt rather than waiting for someone else to.
Prove It and Make It Felt
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Make the Value Legible: Decide how a prediction reaches the customer so they understand it, trust it, and act on it—then report realised impact back to Product and the business in numbers that hold up.
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Measure Honestly: Define offline and online evaluation for model quality, drift, and reliability, and design the A/B tests or causal analyses that prove a feature improved customer outcomes.
Deliver
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Ship and Operate: Deployment, testing, versioning, monitoring, and drift detection. Delivery is part of the job, not a handoff.
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Embed with Product: Partner with Product Managers and Software Engineers to put ML inside real product workflows—and say clearly when ML isn't the answer.
Who We Are
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The Owner: You measure your work by whether customers made better decisions, not by whether the model shipped.