Data Scientist

Data Scientist Location: London/Edinburgh (Hybrid, 2 days in office) Day Rate: £467 Inside IR35 Duration: 6 months The Role In this role, you will uncover insights and intelligence that help customers in dynamic, data-intensive industries operate, scale, and innovate. You will develop robust, future-ready machine learning and analytical models that enable predictive insights, automation, and data-driven decision making across complex digital transformation programmes. Working with modern data platforms, high-quality datasets, and advanced AI frameworks, you will collaborate with engineering, product, and analytics teams to build models that are accurate, explainable, and scalable. The role empowers you to shape end-to-end analytical ecosystems, enhancing decision quality, strengthening operational resilience, and guiding organisations toward an AI-enabled future. Key Responsibilities • Explore, clean, and analyse large, complex datasets to uncover patterns, trends, and opportunities • Develop, train, and validate machine learning, statistical, and predictive models to solve business problems • Design and run experiments such as A/B tests, hypothesis tests, and simulations to quantify outcomes • Collaborate with data engineers, analysts, product managers, and domain experts to translate business requirements into modelling tasks • Build end-to-end ML pipelines, including feature engineering, preprocessing, and deployment-ready outputs • Apply advanced techniques such as NLP, time series forecasting, anomaly detection, optimisation, or LLM/GenAI methods • Implement model evaluation frameworks using offline metrics, cross-validation, online experiments, and human-in-the-loop feedback • Communicate insights through dashboards, visualisations, written summaries, and presentations for technical and non-technical stakeholders • Ensure models are interpretable and explainable, providing transparency into key drivers and assumptions • Work with engineering teams to deploy models into production and monitor, retrain, or recalibrate as data and conditions evolve Essential Skills, Knowledge and Experience • 5–12 years of experience as a Data Scientist • Hands-on experience with GenAI, Gemini, or open-source LLMs and developing GenAI applications for code translation, text extraction, summarisation, and SDLC optimisation • Experience with AI agents, chatbots, RAG (retrieval-augmented generation), and vector databases (PG vector, Croma DB) • Familiarity with GenAI performance evaluation tools such as Pegasus, Ragas, and DeepEval • Ability to create conversational interfaces with React JS or other frontend components, develop and deploy AI agents using LangGraph, ADK, A2A, MCP • Strong programming skills in Python (LangChain/LangGraph/LangSmith frameworks) and TypeScript (preferred) • Solid understanding of LLMs, prompt engineering, and graph-based workflows • Knowledge and implementation of input/output guardrails for hallucination, PII filtering, HAP, and bias mitigation • Experience implementing security best practices and mitigating spikes, DDoS attacks, and other threat scenarios • Knowledge of API gateways, ISTIO, and diagnosing end-to-end communication failures • Hands-on experience with API development and microservices architecture Desirable Skills, Knowledge and Experience • Strong experience applying machine learning, statistical modelling, and predictive analytics to real-world business problems • Ability to resolve end-to-end connectivity and data integration issues in cross-functional teams • Experience working with large, complex datasets, including 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 validation, optimisation, deployment, and monitoring • Understanding of MLOps practices, including model versioning, registries, CI/CD for ML, and automated workflows • Ability to translate business problems into analytical tasks and communicate insights clearly • Knowledge of data governance, quality, lineage, ethics, privacy, and responsible AI principles • Comfort working with cloud platforms (GCP preferred) for scalable model training and deployment • Growth-oriented mindset with enthusiasm for exploring new algorithms, tools, and emerging AI/ML techniques