Data Scientist

Location: London, UK (Hybrid-2 days a week from office)

6 months contract position

The Role

In this role, you will uncover the 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. With access to modern data platforms, high quality datasets, and advanced statistical and AI frameworks, you will work closely with engineering, product, and analytics teams to build solutions that are accurate, explainable, and scalable. This role empowers you to shape end to end analytical ecosystems accelerating delivery, enhancing decision quality, strengthening operational resilience, and guiding organisations toward a more insight rich, AI enabled future.

Your 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.

Your Profile

Essential skills/knowledge/experience:

• 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.
• 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 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 skills/knowledge/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.