JELLYFISH★3.5 (32)· London

The role focuses on building and extending an agentic delivery stack using tool APIs, MCP servers, AI harnesses, agentic operating systems, and Python. You will drive agentic delivery and adoption both inside Jellyfish, a global digital marketing agency, and with its enterprise clients. This is a 4-month fixed-term contract.

PlayStation★3.8 (164)· London

This role involves working with TensorFlow or PyTorch, Python, and libraries for computer vision and graphics. As part of the Visual Computing Group (VCG), you will develop generative models to advance game rendering and live video streaming through neural networks and machine learning R&D.

Work with Python, PyTorch, JAX, CUDA, Docker, Kubernetes, Slurm, Ray, and Linux on GPU computing. The Molecular Intelligence team builds ML and physics-based infrastructure for drug discovery, including biophysics, chemistry, cofolding models, simulation workflows, and agent-usable scientific pipelines.

Work with Python, RDKit, Biopython, OpenMM, MDAnalysis, and open-source computational chemistry workflows for docking, virtual screening, SAR, QSAR, ADMET, and cheminformatics. You will guide and evaluate AI-driven drug discovery workflows, ensuring agent-generated optimization plans and modeling are chemically sound, and lead computational chemistry strategy on active drug programs when needed.

Relay Technologies★2.0 (9)· London

At Relay Technologies you will build and own time-series forecasting models for parcel volume, inbound international volume, and parcel size/weight, using Python and ML frameworks. You will work on the Network squad’s demand-forecasting engine that predicts parcel movement across sortation, middle-mile, and last-mile operations, directly affecting cost per parcel. This is a hands-on individual-contributor role, serving as the technical anchor for Demand Forecasting, collaborating with a Senior Data Scientist, ML Engineer, and Analyst.

You will work with Python, SQL, BM25, vector search, reranking, RAG, and agentic systems to own and evolve Corestream, Kharon’s risk intelligence feed that surfaces entity-level insights and risk narratives for customers in global security, sanctions, and financial crimes.

Python, SQL, BM25, vector search, reranking, and RAG pipelines powering agentic systems. You will own and evolve Corestream, Kharon’s risk intelligence feed, redesigning semantic search and narrative generation for global-security insights.

Build and deploy AI agents for geopolitical and financial risk intelligence, working with Python, SQL, RAG pipelines, BM25, vector/hybrid retrieval, APIs, Docker, Kubernetes, GitHub Actions, and AWS services like RDS, EKS, and S3. Kharon provides risk-analysis tools for global financial institutions and multinationals, covering sanctions, export controls, and threat identification.

Work with Python, SQL, graph databases, graph ML, Docker, Kubernetes, and LLMs. Own and evolve Kharon's risk propagation engine that analyzes entities in a knowledge graph to surface risk signals for global security clients.

HarbourVest★3.1 (16)· London

This role involves productionizing Data Science and AI prototypes using Python, containerized or cloud-based delivery, Snowflake, Power BI, Git, and CI/CD pipelines. You will design analytics workflows and support legacy solutions, partnering with global teams to bridge raw data and analytics-ready assets at HarbourVest, a private markets investment firm.

Moody's★3.6 (589)· London

Lead validation of credit rating models and AI model risk management at Moody's, working with R, Python, MATLAB, SQL, and preferably C++. The MRG Quantitative Review team independently validates quantitative models and scorecards supporting credit ratings, with a focus on Generative and Agentic AI risks. Requires 10+ years in model development, validation, and risk analytics.

Chubb Insurance★3.7 (1.3k)· London

You will use Python, Hugging Face Transformers, LangChain, LlamaIndex, PyTorch, TensorFlow, Docker, and cloud platforms to build and deploy LLM, RAG, and conversational AI applications. As part of Chubb Insurance's Global Analytics team, you'll design production-ready AI solutions for business value.

Meta★4.0 (1.1k)· London

Work with languages such as C++, Java, Swift, Kotlin, Python, JavaScript, and Hack to build and optimize user-facing applications across mobile and web. As a member of Meta’s product engineering teams, you’ll help advance its platforms—Facebook, Instagram, WhatsApp, and immersive AR/VR experiences—that connect billions globally.

Google★4.3 (6.2k)· London

Work with C++, Python, or Rust to build the Google Cloud Universal Ledger (GCUL), a payments platform addressing cost, speed, and fragmentation. Implement critical ledger capabilities including consensus, transaction dissemination, and data privacy via homomorphic cryptography and zero-knowledge proofs.

Infosys★3.8 (17k)· London

You will design, implement, and optimize CI/CD pipelines using the Harness platform, including CI, CD, STO, Chaos Engineering, CCM, and Harness AI Agents, with Kubernetes, Terraform, and cloud platforms. This role leads modern DevOps transformations within Infosys, a global digital services and consulting firm.

Tesco★3.5 (25k)· London

You will work with Java, Spring Boot, Kafka, Apache Flink, Apache Beam, NoSQL databases, Redis, and Apache Camel. The Tesco Personalisation team builds real-time, low-latency services for recommendations, offers, and experimentation across web, mobile, and in-store channels using streaming data and ML.

You use Python and supervised machine learning on tabular data to own and build production credit models that decide which small businesses iwocaPay’s B2B Buy Now Pay Later product can fund. You co-own the full credit modelling stack with one other data scientist.

You'll work with Python, SQL, PySpark, and the PyData stack to build machine learning and analytics solutions for McKinsey's clients across industries, translating business challenges into statistical models and deploying them to production. QuantumBlack, AI by McKinsey is a data science and AI consulting firm applying advanced analytics to solve complex client problems. You'll collaborate with Data Scientists, Engineers, Designers, and Product Managers globally while based in London, with exposure to R&D projects and conference presentations.

Work with Python, Snowflake, Power BI, Git, CI/CD, and cloud deployment to productionize data science and AI prototypes. You will bridge raw data ingestion to analytics-ready assets, building and maintaining analytics workflows and dashboards for a global investment firm.

You will work with AWS, dbt, Spark, SQL, Kafka, Flink, and Airflow on a modern event-driven lakehouse using medallion architecture. The team builds and evolves the next-generation data platform for FDJ UNITED’s Sportsbook product, powering analytics, trading insight, and machine learning across multiple brands and markets.

Work with TensorFlow or PyTorch, Python, and related computer vision/graphics libraries on neural network architectures (convolutional, recurrent, transformer, diffusion-based). The Visual Computing Group (VCG) researches and develops generative models for game rendering and live video streaming to advance state-of-the-art runtime efficiency, visual quality, and latency.

Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.

Use Python, SQL, BM25, vector search, reranking, and RAG pipelines to evolve Kharon's Corestream risk intelligence feed—a platform surfacing entity-level insights and topic-based risk narratives, integrating graph-based outputs into semantic search and narrative generation.

Lead a team of Data Scientists and ML Engineers building AI solutions for financial markets using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, Python, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, Azure AI Foundry, AWS AI Services, and MLOps/LLMOps practices.

You will own Relay’s Demand Forecasting models, working hands-on with Python, ML frameworks, and forecasting tools to predict parcel volume, weight, and size at outcode granularity out to D30. You set the technical direction for the Network squad’s forecasting engine, which drives sortation, middle-mile, last-mile, and expansion decisions. The centralised data team has ~30 people, with you acting as the senior IC anchor, mentoring a Senior Data Scientist and an Analyst.