You will design, build, and maintain data pipelines on Google Cloud Platform using BigQuery, Cloud Storage, Snowflake, SQL, dbt, and Python. As part of WPP Media’s global team, you will support its AI-driven media operating unit by transforming complex business requirements into reliable data assets for client analytics products.

The Senior Analyst will use Excel, data visualisation, forecasting, scenario analysis, market data and AI tools (Python optional) to produce geopolitical insight. The role leads coverage of the Americas for the Geopolitics Team, which briefs Tony Blair, senior TBI leadership, government principals and corporate clients on political, security and market risks.

The role uses data visualisation, forecasting, scenario analysis, market data, Excel, and AI tools (Python a plus). You will join TBI’s Geopolitics Team, providing decision-relevant geopolitical insight on China and Asia Pacific to senior public and corporate clients.

You'll work with Python, PostgreSQL, MySQL, BigQuery, FastAPI, Flask, GCP/AWS, and CI/CD pipelines. You'll join the commercial performance insights squad to build data pipelines and event streams for monitoring algorithmic insurance underwriting, including Monte Carlo simulation.

Develop ML and AI solutions using Python, SQL, and NLU, focusing on model development, data preparation, and experimentation for customer operations. The team builds production AI systems across the full ML lifecycle. NICE Ltd. provides customer experience and financial crime software to global businesses.

Synapri★0.0· London

You will use Python, SQL, PySpark, machine learning, NLP and Generative AI to build scalable data science solutions on an Insights team. The role involves transforming customer interaction data into insights, deploying models in Azure with CI/CD, and using tools like PyTorch, MLflow, DBT and Databricks.

Lead a team of three data scientists and three ML engineers using Python, Kafka, Spark, Flink, and on-device ML to transform phone sensor data (accelerometer, GPS) into driving-behavior models for fair insurance pricing and safety. The role owns the full telematics and pricing stack, reporting to the Director of Data.

You will work with Java microservices, Spring Boot, Python, SQL, Snowflake, dbt, Airflow, LangChain, Sagemaker, and MLflow. The data science team builds ML solutions for risk-based decisioning, pricing, and operational automation in insurtech/fintech. This is a hands-on leadership role—you will lead and develop the team while setting technical standards.

Quantitative researchers will develop mathematical, statistical, and machine-learning models for systematic trading, working in Python, with C++ and SQL as desirable, alongside large-scale data analysis. The role spans full research-to-production, collaborating with portfolio managers and engineers at a global quantitative investment firm. No team size is stated.

Deloitte★3.9 (14k)· London

You will work with Python, NumPy, Pandas, scikit-learn, LangChain, Hugging Face, LlamaIndex, Azure Machine Learning, Foundry, Git, and Agile practices. The team is Deloitte’s Tax Digital Innovation AI & Data Studio, which researches, builds, and delivers analytics and AI-driven solutions for tax and legal clients.

Work with Python, SQL, JavaScript/TypeScript, NumPy, Pandas/Polars, visualization libraries, and deep learning frameworks (PyTorch, JAX, or TensorFlow) to design and deploy full-stack data-driven applications and machine learning solutions. You will be embedded with Arsenal's Men’s First Team Analysis department, building tools that directly support coaching and analysis workflows.

Develops enterprise data platforms using Python, SQL, and Databricks, building ETL/ELT pipelines and working with data lakes and lakehouse architectures.

Use Azure, Databricks, Python, PySpark, SQL and Azure Data Factory to build scalable ETL/ELT pipelines and cloud data solutions. You will design and maintain data systems for a London Market insurer’s enterprise platform, supporting smarter decisions across Underwriting, Finance and Operations.

Work with Python, SQL, SHAP, Airflow, PyFlink, BentoML, Git, Jenkins, Octopus, Docker, JIRA and MLFlow. As a Data Scientist at Zempler Bank, you develop algorithms to protect customers, widen credit access and improve financial outcomes. You implement and monitor models in production.

You will work with Python, NumPy, SciPy, simulation modelling, optimisation under uncertainty, cloud deployment, and machine learning. The team builds an AI operating system for secure environments, taking complex scientific models from prototype to production across government and commercial programmes.

SQL and Python are used to develop pricing strategies, analyse marketing and conversion funnel performance, and evaluate product expansion opportunities. The role supports a fintech unicorn that revolutionises the credit and savings landscape. Mentoring and coaching junior analysts is also part of the position, though the team’s size is unspecified.

You’ll work with QGIS, ArcGIS, GeoPandas, Rasterio, GDAL, Python, and AI-assisted development tools. As part of the ANALYTIQ team, you’ll build geospatial models, reusable scripts, data pipelines, and interactive web tools to accelerate Systemiq’s sustainability projects across energy, materials, nature, and finance.

You will use Python, SQL, and Streamlit on an AWS data platform to clean survey data, build statistical and machine learning models (including NLP), and design interactive dashboards. As a Data Scientist II on the Data Science Consulting team, you will deliver predictive analytics solutions that optimise clients’ marketing effectiveness. You will also manage client relationships and guide more junior team members.

You will work with Python, SQL, AzureML, and XGBoost. You’ll join a small, fast-paced pricing team of 6 at a large insurance company, building alternative pricing models, geospatial models, and residual models from large, messy datasets to improve pricing and customer accessibility.

Use Python, SQL, and production ML pipelines for personalization and recommendations (embeddings, ranking, collaborative filtering) on Muzz, the world's largest Muslim dating and marriage app. You'll also use AI tools, conduct A/B experiments, and work with GCP (BigQuery/VertexAI). The role includes mentoring analysts and data scientists and shaping technical standards.

You will work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, cloud data tools (ideally Azure), and large language models (LLMs) to build predictive models and identify alpha signals. You will join the data science team of a leading investment management company.

You'll work with Microsoft Fabric, PySpark, Python, T-SQL, DAX, Infrastructure as Code, Git, and Azure DevOps. You'll design, build, and maintain data pipelines, lakehouses, and data warehouses within Fabric to drive business intelligence and analytics.

Uses SQL, Python or scripting, BI tools like PowerBI/Looker/Tableau, spreadsheets, and Git to investigate fraud and build dashboards. The role sits in fraud analytics at Moniepoint, an all-in-one financial services platform powering POS transactions and banking across Nigeria and emerging markets.

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.

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.