Data Scientist

Vallum Associates
London, United Kingdom
13 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Faculty AI London, United Kingdom
Hybrid Clearance Required

Data Scientist

Yolk Recruitment Cardiff, South Glamorgan, CF10 2AF, United Kingdom
£60,000 – £70,000 pa

Data Scientist

Guidant Global London, United Kingdom
£600 – £850 pd On-site Clearance Required

Data Scientist

Opus Recruitment Solutions Nottingham, Nottinghamshire, United Kingdom
£400 – £450 pd On-site

Data Scientist

University of Oxford Oxford, South East England, United Kingdom
£39 – £47 pa On-site

Data Scientist

Hays Technology London, United Kingdom
£600 – £1,000 pd
Posted
2 May 2025 (13 months ago)

Role- Data Scientist

Location: London, UK

Type of job : Contract

Work mode : Hybrid- 2 days onsite in a week

Responsibilities:

* Explore, clean, and analyse large, complex datasets to uncover patterns, trends, and opportunities that drive actionable insights.

* Develop, train, and validate machine learning, statistical, and predictive models that solve real business problems and deliver measurable impact.

* Design and run experiments (A/B tests, hypothesis tests, simulations) to evaluate ideas, quantify outcomes, and guide decision‑making.

* Collaborate with data engineers, analysts, product managers, and domain experts to translate business requirements into well‑defined modelling tasks.

* Build end‑to‑end ML pipelines—from feature engineering and preprocessing to deployment‑ready model outputs.

* Apply advanced techniques such as NLP, time‑series forecasting, anomaly detection, optimisation, or LLM/GenAI methods where relevant.

* Implement model evaluation frameworks using offline metrics, cross‑validation, online experiments, and human‑in‑the‑loop feedback loops.

* Communicate insights clearly through dashboards, visualisations, written summaries, and presentations tailored to technical and non‑technical stakeholders.

* Ensure models are interpretable and explainable where required, providing transparency into key drivers and assumptions.

* Work with engineering teams to deploy models into production, monitor performance, and retrain or recalibrate as data and conditions change.

Essential skills:

Hands-on experience with GenAI, Gemini or Open source LLMs and develop GenAI applications for Code Translation, Text Extraction, Summarisation and SDLC Optimization etc.

* Hands-on Experience with AI Agents, Chat bots, RAG (Retrieval-Augmented Generation), and vector databases. ( PG vector / croma DB )

* Hands-on Experience with GenAI Performance Evaluation tools like Pegasus, Ragas, DeepEval

* Create Conversational Interface with React JS or other Frontend components, Develop and deploy AI agents using LangGraph and ADK, A2A, MCP

* Strong programming skills in Python (experience with LangChain/LangGraph / LangSmith frameworks) and TypeScript ( preferable )

* Solid understanding of LLMs, prompt engineering, and graph-based workflows.

* Knowledge and implementation of Input and Output guardrails in addressing Hallucination, PII filtering, HAP and Bias etc.

* Implemented security best practices, Experience to address spikes and Denial of wallet attacks, DDoS attack and other Spike arrest strategies

* Knowledge of API Gateways and ISTIO , ability to Diagnose and intercept failures in End to End communication

* Hands-on Experience with API Development and Microservices architecture

Desirable experience:

* Strong experience applying machine learning, statistical modelling, and predictive analytics to real‑world business problems.

* Collaborate with cross-functional teams to ability to resolve end to end connectivity and Data Integrations

* Experience working with large, complex datasets, including data cleaning, feature engineering, and exploratory data analysis.

* Familiarity with LLMs, NLP techniques, and GenAI frameworks, including embeddings, prompt engineering, or fine‑tuning.

* Experience building end‑to‑end ML pipelines, including model validation, optimisation, deployment, and monitoring.

* Understanding of MLOps practices, including model versioning, model registries, CI/CD for ML, and automated training/inference workflows.

* Ability to translate business problems into analytical tasks and communicate insights in a clear, concise manner to technical and non‑technical audiences.

* Knowledge of data governance, including data quality, lineage, ethics, privacy considerations, and responsible AI principles.

* Comfort working with cloud platforms (GCP preferred) for model training, deployment, and scalable compute.

* A growth‑oriented mindset with enthusiasm for exploring new algorithms, tools, and emerging AI/ML techniques

Industry Insights

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

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Where to advertise data science jobs UK in 2026: the specialist boards, communities and channels that actually reach senior and lead data science talent. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Data Science Jobs UK 2026: What to Expect Over the Next 3 Years

Data Science Jobs UK 2026: roles, salaries and the trends shaping UK data science hiring over the next three years — from MLE crossover to GenAI workflows. Data science has spent the past decade being described as the sexiest job of the twenty-first century. By 2026, the reality is both more nuanced and more interesting than that label ever suggested. The discipline has matured, fragmented, deepened, and in some respects reinvented itself — and the jobs market has changed with it in ways that create genuine opportunity for those who understand what employers actually want, and genuine difficulty for those still operating on assumptions formed five years ago. The data science jobs market of 2026 is not simply a larger version of what it was three years ago. The generalist data scientist — equally comfortable wrangling data, building models, and presenting insights to the board — is giving way to a more specialised landscape where employers know exactly what problem they are trying to solve and are looking for candidates with the specific depth to solve it. Machine learning engineering, causal inference, experimentation, AI product development, and domain-specific applied science have all emerged as distinct career tracks within what was previously a single, loosely defined profession. At the same time, the arrival of large language models and the broader AI capability wave has both threatened and created data science roles in equal measure. Some of the work that junior data scientists spent their early careers doing — data cleaning, exploratory analysis, basic model building — is being partially automated by AI tooling. But the demand for practitioners who can evaluate AI systems rigorously, apply statistical thinking to complex business problems, and build the data foundations on which AI depends has grown considerably. The candidates who will thrive over the next three years are those who understand where the discipline is heading — which specialisms are attracting the most investment, which technologies are reshaping what data scientists are expected to build and know, and how to position a data science career that will remain valuable as the field continues to evolve around them. This article breaks down what the UK data science jobs market is likely to look like through to 2028 — covering the titles emerging right now, the technologies driving employer demand, the skills that will matter most, and how to position your career ahead of the curve.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

New Data Science Employers to Watch in 2026: a UK and international shortlist of analytics and AI companies hiring data scientists, ML engineers and analysts. Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.