You will use PyTorch, JAX, and scikit-learn to build ML models in low-data regimes for drug discovery and molecular optimization. You will develop active learning, meta-learning, and uncertainty-aware approaches, designing data acquisition strategies that guide experiments, simulations, and compound selection.

The role involves training next-generation cofolding models using PyTorch, JAX, Boltz, AlphaFold-style methods, equivariant GNNs, contrastive learning, self-supervised representation learning, and DEL datasets. The scientist collaborates with ML researchers, chemists, biophysicists, and data engineers to improve protein-ligand binding prediction and AI-driven drug discovery. Lila Sciences builds an AI-powered operating system for autonomous scientific discovery across medicine, materials, and energy.

Design, build, and deploy quantitative models using Python, R, and libraries (numpy, pandas, scikit, pytorch, tidyverse, caret, ggplot) plus NLP tools, SQL, AWS, Sagemaker, DataBricks, Snowflake, MLFlow, and Weights & Biases. The team creates systematic country risk analytics and ML/NLP pipelines for BMI’s market insights, supporting clients in risk assessment.

King is collecting profiles for future AI/ML, Data Science, and Analytics roles. Work may involve Python, SQL, Spark, PyTorch, TensorFlow, scikit-learn, cloud platforms and ML pipelines, supporting mobile games like Candy Crush Saga played by over 200 million monthly active users.

Design, develop, and evaluate machine learning models using Python, PyTorch, TensorFlow, statistical modelling, deep learning, and multimodal AI for the ASOS AI Demand Forecasting platform. This platform provides demand forecasts to power replenishment, AI-powered pricing, and supply chain optimisation. You will also mentor other scientists and provide technical leadership on complex modelling projects.

On Ag★2.9 (7)· London

Work with Python, PyTorch, JAX, TensorFlow, GCP/AWS, Kafka, and MLOps to build real-time personalization and recommendation engines. Lead data science initiatives that design, train, and deploy customer-level algorithms for On’s global D2C/e-commerce platform.

Fitch Solutions★3.0 (2)· London

As a Data Scientist at BMI (a Fitch Solutions business), you will design, build, and deploy quantitative models using Python and R (e.g., numpy, pandas, scikit, pytorch) to power advanced analytics. You will develop and maintain ML/NLP and data pipelines, working with Economists and Analysts to build risk models and generate insights from structured and unstructured data.

You will work with Python, PyTorch, JAX, TensorFlow, GCP/AWS, Kafka, and end-to-end MLOps to build and deploy batch and real-time machine learning models. As a Principal Data Scientist at On, you set the strategic direction for personalization and customer algorithms that directly impact the global D2C customer experience across web, app, and messaging.

Design and build production-grade ML systems using MCP servers, recommendation systems, RAG pipelines, LLMs, and foundation models with Python, TensorFlow, PyTorch, FastAPI, and LangChain. You will own the end-to-end AI lifecycle for a sales data platform, deploying models into batch/real-time environments to drive forecasting, segmentation, and early warning systems. This role focuses on embedding context-aware intelligence into decision-making tools for business stakeholders.

You’ll work with Python, pandas, scikit-learn, PyTorch, Hugging Face, and LLMs (fine-tuning, prompt engineering). The company builds an AI operating system for industries with fragmented legacy data, producing applied ML models for forecasting, optimisation, and recommendation.

FDM Group★2.9 (270)· London

You'll work with Python, pandas, JSON, REST APIs, LLMs, RAG, vector embeddings, and semantic search to build data ingestion pipelines and scalable platforms that enable AI-powered analytics and decision support within a leading global investment bank.

You'll lead a 4–5 person team building machine learning systems using Python, TensorFlow, PyTorch, JAX, AWS, Docker, and Kubernetes for Super's Playstack platform, which powers sports betting and gaming experiences across Europe and Latin America. The role combines technical contribution, project management, and people leadership, focusing on predictive analytics, personalization, fraud detection, and real-time recommendations that enhance customer engagement in online gambling and sports betting.

You will work with Python, TensorFlow, PyTorch, and deep learning models on time series analysis, digital twins, and surrogate modelling. The company applies machine learning to real-world energy datasets to optimize performance and decision-making.

Sainsbury’s★3.5 (16k)· London

In this Senior Data Engineer role you will design and build scalable data pipelines and platforms using PySpark or PyTorch on AWS EMR, Snowflake, Docker, Airflow, Kubernetes, SQL/NoSQL databases and CI/CD with GitHub Actions. You will partner with Data Scientists to make data reliable and production-ready for machine learning solutions, and mentor junior and mid-level engineers.

HeliosX★3.5 (19)· London

You'll work with Snowflake, Hex, Claude, and MLFlow or SageMaker in Python to build and deploy clinical prediction, risk stratification, and treatment-optimization models for a digital healthcare platform that personalizes patient journeys.

Rail Safety and Standards Board★3.8 (17)· London

Senior Data Engineer roles at RSSB using SQL, Python, Airflow, Databricks, Power BI/Fabric, Git, and FastAPI to build data pipelines, APIs, and data stores for analytics and data-driven tools from rail-industry safety, performance, and operational datasets.

Build and scale data pipelines for large video generation models using AWS S3, DynamoDB, Kubernetes, Python, and PyTorch. You will design annotation workflows across MTurk and Prolific, and train smaller supporting models for data filtering and quality assessment. Cantina Labs is a social AI company developing real-time character models.

Design and build production-grade ML systems including MCP servers, recommendation engines, and RAG pipelines. Work with Python, LLMs, LangChain, FastAPI, PyTorch, Spark, SQL, vector databases, and AWS/GCP on a sales data platform to create chatbots, semantic search, and forecasting models. Own the full AI/ML lifecycle—from problem definition to deployment and monitoring—to drive sales strategy and decision-making.

Barclays★3.9 (6.9k)· London

Lead a data science team at Barclays, working with Python, NumPy, Pandas, scikit-learn, and PyTorch to design, develop, and deploy production-grade AI and machine learning solutions. The role builds scalable, governed AI systems to extract insights from the bank’s data, informing strategic decisions and driving innovation. You will set technical direction and coach a team of data scientists.

You will work with Python, SQL, TensorFlow, PyTorch, Scikit-learn, MLOps tools, cloud platforms like Azure/AWS/GCP, and Generative AI frameworks. The team delivers end-to-end AI and machine learning solutions for enterprise clients as part of a global consulting and technology services organization.

You'll lead Python and FastAPI development on Lendable's Data Team, building data pipelines, APIs, and services deployed via Docker, Kubernetes, and AWS with ArgoCD. The fintech company rebuilds consumer finance products (loans, credit cards, car finance) to serve UK and US markets faster than traditional banks. You'll mentor a small team of 1–2 engineers while owning technical direction and remaining hands-on with code.

You will work with Databricks, Neo4j, Python, FastAPI, Spark/PySpark, SQL, and cloud-native platforms. The role focuses on building graph-driven data platforms and backend data services for advanced analytics and knowledge graph use cases at Acuity Analytics, a data and digital transformation solutions provider.

FDM Group★2.9 (270)· London

Python, pandas, JSON, REST APIs, FastAPI, LLMs, RAG, vector embeddings, semantic search, Plotly, and Plotly Dash. You will build scalable data platforms and AI-powered solutions for a leading global investment bank, enabling intelligent scenario modelling and decision support.

You will work with Python, SQL, XGBoost, PyTorch, Git, Jupyter, and MLOps tools to develop, test, deploy, and maintain production ML and statistical models as part of a team executing an international bank’s data science strategy and AI roadmap.

You will use Python, C++, Linux/UNIX, PyTorch, JAX, or TensorFlow to develop and deploy advanced ML models (Deep Learning, Reinforcement Learning, sequence models) for forecasting asset volatility. Part of a specialized research team, you will perform greenfield research on unstructured datasets to discover new alpha signals for trading at a systematic investment firm.