Remote jobs in United Kingdom

Location - We are flexible : onsite, hybrid or fully remote, depending on what works for you and the client, UK or Netherlands based.

This is not a role where you build clever models that never get used. Your focus is on creating measurable value for clients using data science, machine learning and GenAI, in a consulting and advisory context.

You will own work from the very beginning, asking questions like "What value are we trying to create here?" and "Is this the right problem to solve?" through to "It is live, stakeholders are using it and we can see the impact in the numbers."

You will work fairly independently and you will also be someone that more junior team members look to for help and direction. A big part of the job is taking messy, ambiguous business and technical problems and turning them into clear, valuable solutions that make sense to the client.

You will do this in a client facing role. That means you will be in the room for key conversations, providing honest advice, managing expectations and helping clients make good decisions about where and how to use AI.

• Meeting with stakeholders who may not be clear on what they really need
• Using discovery sessions, workshops and structured questioning to uncover the real business problem
• Framing success in terms of value. For example higher revenue, lower cost, reduced risk, increased efficiency or better customer experience
• Translating business goals into a clear roadmap of data and AI work that everyone can understand
• Advising clients when AI is not the right solution and suggesting simpler or more cost effective alternatives

• Acting as a trusted advisor to product owners, heads of department and executives
• Helping clients prioritise use cases based on value, feasibility and risk
• Communicating trade offs in a simple way. For example accuracy versus speed, innovation versus compliance, cost versus impact
• Preparing and delivering client presentations, proposals and updates that tell a clear story
• Supporting pre activities where needed, such as scoping work, estimating effort and defining outcomes
• Managing client expectations, risks and dependencies so there are no surprises

Once the problem and value are clear, you will design and deliver production ready ML and GenAI solutions. That includes :

• Designing and building data pipelines, batch or streaming, that support the desired outcomes
• Working with engineers and architects so your work fits cleanly into existing systems
• Making sure what you build is reliable in production and moves the needle on agreed metrics, not just offline benchmarks
• Explaining design decisions to both technical and non technical stakeholders

You will work with GenAI in ways that are grounded in real use cases and business value :

• Building RAG systems that improve search, content discovery or productivity rather than existing for their own sake
• Implementing guardrails so models do not leak PII or generate harmful or off brand content
• Defining and tracking the right metrics so you and the client can see whether a GenAI solution is useful and cost effective
• Fine tuning and optimising models so they perform well for the use case and budget
• Designing agentic workflows where they genuinely improve outcomes rather than add complexity
• Helping clients understand what GenAI can and cannot do in practice

You will set up the foundations that protect value over time :

• Experiment tracking and model versioning so you know what works and can roll back safely
• CI / CD pipelines for ML so improvements reach users quickly and reliably
• Monitoring and alerting for models and data so you can catch issues before they damage trust or results
• Communicating operational risks and mitigations to non technical stakeholders in plain language

Security, quality and compliance

You will help make sure :

• Data is accurate, traceable and well managed so decisions are sound
• Sensitive data is handled correctly, protecting users and the business
• Regulatory and compliance requirements are met, avoiding costly mistakes
• Clients understand the risk profile of AI solutions and the controls in place

You will be a bridge between technical and non technical teams, inside our organisation and on the client side. That means :

• Explaining complex ML and GenAI ideas in plain language, always tied to business outcomes
• Working closely with product managers, engineers and business stakeholders to prioritise work that matters
• Facilitating workshops, playback sessions and show and tells that build buy in and understanding
• Coaching and supporting junior colleagues so the whole team can deliver more value
• Representing the company professionally in client meetings and at industry events

• Around 3 to 6 years of experience shipping ML or GenAI solutions into production
• A track record of seeing projects through from discovery to delivery, with clear impact
• Experience working directly with stakeholders or clients in a consulting, advisory or product facing role

• A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering
• or
• Equivalent experience that shows you can deliver results

• Strong Python and SQL, with clean, maintainable code
• Solid understanding of ML fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics
• Experience with PyTorch or TensorFlow

• Hands on experience with LLM APIs or open source models such as Llama or Mistral
• Experience building RAG systems with vector databases such as FAISS, Pinecone or Weaviate
• Ability to evaluate and improve prompts and retrieval quality using clear metrics
• Understanding of safety practices such as PII redaction and content filtering
• Exposure to agentic frameworks

• Comfortable working in at least one major cloud provider. AWS, GCP or Azure
• Familiar with Docker and CI / CD pipelines
• Experience with managed ML platforms such as SageMaker, Vertex AI or Azure ML

• Experience with data warehouses such as Snowflake, BigQuery or Redshift
• Workflow orchestration using tools like Airflow or Dagster
• Experience with MLOps tools such as MLflow, Weights and Biases or similar
• Awareness of data and model drift, and how to monitor and respond to it before it erodes value

• Familiar with secure development practices
• Experience working with sensitive data and following relevant policies and regulations

• You are comfortable in client facing settings and can build trust quickly
• You can talk with anyone from a CEO to a new data analyst, and always bring the conversation back to business value
• You can take a vague, messy business problem and turn it into a clear technical plan that links to outcomes and metrics
• You are happy to push back and challenge assumptions respectfully when it is in the client’s best interest
• You like helping other people grow and are happy to mentor junior colleagues
• You communicate clearly in writing and in person

Do not rule yourself out if you do not have these. They are a bonus, not a checklist.

• Experience with Delta Lake, Iceberg, Spark or Databricks, Palantir
• Experience optimising LLM serving with tools such as vLLM, TGI or TensorRT LLM
• Search and ranking experience. For example Elasticsearch or rerankers
• Background in time series forecasting, causal inference, recommender systems or optimisation
• Experience managing cloud costs and IAM so value is not lost to waste
• Ability to work in other languages where needed. For example Java, Scala, Go or bash
• Experience with BI tools such as Looker or Tableau
• Prior consulting experience or leading client projects end to end
• Contributions to open source, conference talks or published papers that show your ability to share ideas and influence the wider community

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