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