Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist- Gen AI

Scrumconnect Limited
London
1 week ago
Create job alert

London, United Kingdom | Posted on 12/09/2025

We’re hiring aData Scientist with strong Generative-AI experienceto design, build, and ship AI-powered tools end-to-end. You’ll work in a small, multi-disciplinary team and take ownership from discovery to deployment: scoping use-cases, building prototypes, hardening them for production, and putting the right evaluation and governance around them.

What you’ll do

  • Build GenAI tools end-to-end(independently): chat/assistants, document Q&A (RAG), summarisation, classification, extraction, and workflow/agent automations.
  • Own evaluation & safety: create offline/online eval sets, measure faithfulness/hallucination, bias, safety, latency and cost; add guardrails and red-teaming.
  • Productionise: package as services/APIs or lightweight apps (e.g., Streamlit/Gradio/React), containerise, and integrate via CI/CD.
  • Data pipelines: design chunking/embedding strategies, pick vector stores, manage prompt/versioning, and monitor drift & quality.
  • Model strategy: select and mix providers (hosted and open-source), fine-tune where it’s sensible, and optimise for cost/perf/privacy.
  • Stakeholder enablement: translate problems into measurable KPIs, run discovery, document clearly, and hand over maintainable solutions.
  • Good practice: apply data ethics, security and privacy by design; align to service standards and accessibility where relevant.

Tech you’ll likely use

  • LLM frameworks: LangChain, LlamaIndex (or similar)
  • Cloud & Dev: Azure/AWS/GCP, Docker, REST APIs, GitHub Actions/CI
  • Data & MLOps: BigQuery/Snowflake, MLflow/DVC, dbt/Airflow (nice to have)
  • Front ends (for internal tools): Streamlit / Gradio / basic React

Must-have experience

  • 7+ yearsin Data Science/ML, includinghands-on delivery of GenAI products(not just PoCs).
  • Proven ability toship independently: from idea → prototype → secure, supportable production tool.
  • StrongPython & SQL; solid software engineering habits (testing, versioning, CI/CD).
  • Practical LLM skills: prompt design,RAG, tool/function calling,evaluation & guardrails, and prompt/model observability.
  • Sound grasp ofstatistics/experimentation(A/B tests, hypothesis testing) and communicating impact to non-technical audiences.
  • Data governance, privacy and secure handling of sensitive data.

Nice to have

  • Experience in regulated or public-sector-like environments.
  • Front-end skills to craft usable internal UIs.

How to apply

Send your CV (referencingDS-GENAI) to the Recruitment Team. Shortlisted candidates will complete a brief technical exercise or portfolio walk-through focusing ona GenAI tool you built and shipped.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Palantir

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

Why the UK Could Be the World’s Next Data Science Jobs Hub

Data science is arguably the most transformative technological field of the 21st century. From powering artificial intelligence algorithms to enabling complex business decisions, data science is essential across sectors. As organisations leverage data more rapidly—from retailers predicting customer behaviour to health providers diagnosing conditions—demand for proficiency in data science continues to surge. The United Kingdom is particularly well-positioned to become a global data science jobs hub. With world-class universities, a strong tech sector, growing AI infrastructure, and supportive policy environments, the UK is poised for growth. This article delves into why the UK could emerge as a leading destination for data science careers, explores the job market’s current state, outlines future opportunities, highlights challenges, and charts what must happen to realise this vision.