Data Scientist in EC1

Job Description

Role- Data Scientist\n\nLocation: London, UK\n\nType of job : Contract\n\nWork mode : Hybrid- 2 days onsite in a week\n\nResponsibilities:\n\n * Explore, clean, and analyse large, complex datasets to uncover patterns, trends, and opportunities that drive actionable insights.\n\n * Develop, train, and validate machine learning, statistical, and predictive models that solve real business problems and deliver measurable impact.\n\n * Design and run experiments (A/B tests, hypothesis tests, simulations) to evaluate ideas, quantify outcomes, and guide decision‑making.\n\n * Collaborate with data engineers, analysts, product managers, and domain experts to translate business requirements into well‑defined modelling tasks.\n\n * Build end‑to‑end ML pipelines—from feature engineering and preprocessing to deployment‑ready model outputs.\n\n * Apply advanced techniques such as NLP, time‑series forecasting, anomaly detection, optimisation, or LLM/GenAI methods where relevant.\n\n * Implement model evaluation frameworks using offline metrics, cross‑validation, online experiments, and human‑in‑the‑loop feedback loops.\n\n * Communicate insights clearly through dashboards, visualisations, written summaries, and presentations tailored to technical and non‑technical stakeholders.\n\n * Ensure models are interpretable and explainable where required, providing transparency into key drivers and assumptions.\n\n * Work with engineering teams to deploy models into production, monitor performance, and retrain or recalibrate as data and conditions change.\n\nEssential skills:\n\nHands-on experience with GenAI, Gemini or Open source LLMs and develop GenAI applications for Code Translation, Text Extraction, Summarisation and SDLC Optimization etc.\n\n * Hands-on Experience with AI Agents, Chat bots, RAG (Retrieval-Augmented ), and vector databases. ( PG vector / croma DB )\n\n * Hands-on Experience with GenAI Performance Evaluation tools like Pegasus, Ragas, DeepEval\n\n * Create Conversational Interface with React JS or other Frontend components, Develop and deploy AI agents using LangGraph and ADK, A2A, MCP\n\n * Strong programming skills in Python (experience with LangChain/LangGraph / LangSmith frameworks) and TypeScript ( preferable )\n\n * Solid understanding of LLMs, prompt engineering, and graph-based workflows.\n\n * Knowledge and implementation of Input and Output guardrails in addressing Hallucination, PII filtering, HAP and Bias etc.\n\n * Implemented security best practices, Experience to address spikes and Denial of wallet attacks, DDoS attack and other Spike arrest strategies\n\n * Knowledge of API Gateways and ISTIO , ability to Diagnose and intercept failures in End to End communication\n\n * Hands-on Experience with API Development and Microservices architecture\n\nDesirable experience:\n\n * Strong experience applying machine learning, statistical modelling, and predictive analytics to real‑world business problems.\n\n * Collaborate with cross-functional teams to ability to resolve end to end connectivity and Data Integrations\n\n * Experience working with large, complex datasets, including data cleaning, feature engineering, and exploratory data analysis.\n\n * Familiarity with LLMs, NLP techniques, and GenAI frameworks, including embeddings, prompt engineering, or fine‑tuning.\n\n * Experience building end‑to‑end ML pipelines, including model validation, optimisation, deployment, and monitoring.\n\n * Understanding of MLOps practices, including model versioning, model registries, CI/CD for ML, and automated training/inference workflows.\n\n * Ability to translate business problems into analytical tasks and communicate insights in a clear, concise manner to technical and non‑technical audiences.\n\n * Knowledge of data governance, including data quality, lineage, ethics, privacy considerations, and responsible AI principles.\n\n * Comfort working with cloud platforms (GCP ) for model training, deployment, and scalable compute.\n\n * A growth‑oriented mindset with enthusiasm for exploring new algorithms, tools, and emerging AI/ML techniques