Top Data Jobs in London

Build and migrate programme data models and pipelines in Snowflake, including Snowflake ML implementations. Transform PySpark pipelines, assess Snowflake-native alternatives, validate model performance with Data Scientists, document findings, support Pre-Prod to Prod cutover, and resolve issues during the warranty period.

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, and catalog registration; investigate and resolve pipeline issues; review code; trace data lineage; support data security, governance, and reverse ETL integrations; collaborate with stakeholders; and mentor junior data engineers.

Lead end-to-end data science initiatives for a two-sided automotive marketplace. Build, deploy, monitor, and iterate on machine learning and GenAI solutions involving pricing, demand prediction, marketing optimization, recommendations, document processing, and intelligent search. Partner with commercial, marketing, product, finance, engineering, and operations teams to translate business problems into production-ready solutions and measurable outcomes. Contribute to data science standards and mentor junior team members.

The Enterprise Account Executive will drive new enterprise revenue, engage with senior decision-makers, and execute growth strategies for Data and AI training solutions.

The role involves supporting customer engagements, optimizing processes, and leveraging software and data to improve business performance in Life Sciences Consulting.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Supports clinical research studies, primarily type 1 diabetes projects, by administering and validating research data, maintaining databases, running queries, coding medical information, producing reports, and resolving data discrepancies. The role also manages study documentation, literature and bibliographic databases, participant testing kits, supplies, quality checks, and study communications. It develops procedures, tests database applications, supports Good Clinical Practice compliance, and provides cover for study coordinators while helping supervise junior staff.

Analyze player behavior and live-game performance across retention, engagement, progression, funnels, cohorts, churn, and monetization. Build dashboards, define KPIs, investigate anomalies, and communicate insights clearly. Own the internal game analytics platform, ensuring reliable event tracking, consistent metric definitions, and accessible performance views. Collaborate with developers and potentially help transform the platform into a public Roblox analytics product.

Own the strategy, design, and production development of Provenir’s identity graph and graph-driven intelligence. Build pipelines for identity resolution, link analysis, clustering, anomaly detection, ranking, and fraud intelligence. Partner with engineering and product teams to translate business problems into product capabilities, establish data science standards, and ensure quality, scale, and explainability. Mentor colleagues and eventually grow a data science team while remaining hands-on with graph methods, analytical tooling, and pragmatic AI applications.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Develop and evaluate machine learning models for customer feedback, including classification, extraction, summarization, sentiment analysis, retrieval, reranking, and semantic search. Build LLM-powered features, evaluation frameworks, fine-tuning pipelines, and model integrations. Collaborate with Data Science, Engineering, and Product teams to translate requirements into experiments and production solutions, while monitoring research and documenting work clearly.

Leads the enterprise data governance strategy, framework, roadmap, policies, standards, and data quality programs. Establishes metadata, lineage, master data, classification, access, and privacy practices while partnering with IT and Security to support secure AI initiatives. The role resolves cross-functional data issues, aligns governance with regulatory requirements, measures data quality, and develops the governance team in a regulated healthcare or technology environment.

Lead data engineering and platform teams in designing, delivering, and modernizing scalable enterprise data platforms. Make architectural decisions across ingestion, storage, processing, modeling, orchestration, governance, security, and serving. Establish software engineering, testing, CI/CD, infrastructure-as-code, observability, and data quality practices. Mentor engineers, guide clients through complex transformations, contribute to bids and proposals, and help teams apply GenAI with appropriate controls.

Drive sales and adoption of Zscaler's Data Protection portfolio (DLP, CASB). Act as primary customer and partner contact in the UK, collaborate with solution engineers, craft value propositions, present to technical and executive stakeholders, and use account-based strategies and channel partnerships to meet quota.

Build and maintain consumer-credit risk, value, revenue, profit, and customer lifetime value models. Translate ambiguous credit questions into practical analyses and classification or regression solutions using Python and statistical judgment. Partner with Credit Strategy, Product, and Engineering to prioritize work, productionize models, explain technical decisions, and drive alignment. This hands-on individual-contributor role offers mid- and senior-level scope in a collaborative hybrid environment.

Build and operate scalable data pipelines, web scraping systems, and ML infrastructure for training, evaluating, and serving speech AI models. Responsibilities include improving GPU utilization, scheduling, observability, data quality, production reliability, and distributed-system troubleshooting. The role also contributes to MLOps practices and platform roadmap development.

Build and improve Lendable’s data platform by delivering scalable, reliable, secure, and cost-effective data solutions. Partner with product, analytics, and engineering teams to enable fast analytics and insight generation. Responsibilities include developing data-intensive systems, applying modern data architectures, managing cloud infrastructure and infrastructure-as-code, supporting CI/CD, evaluating build-versus-buy decisions, and improving platform operations while working autonomously in an ambiguous environment.

Build and maintain investment data solutions, quantitative models, performance attribution systems, dashboards, and reporting workflows. Validate data quality, automate analytical processes, support backtesting and scenario analysis, apply model governance, and collaborate with investment, technology, and risk teams. The role also evaluates emerging technologies, including artificial intelligence and machine learning, to improve investment research and decision-making.

Lead global sales for Sparta’s Data Marketplace, owning revenue targets, pipeline development, forecasting, pricing, and deal structuring. Sell proprietary and third-party commodity market data to quant teams, trading desks, hedge funds, and analytics groups. Partner with regional sales teams to drive upsell and new-logo opportunities, collaborate with product on roadmap and packaging, and represent the company at industry events. The role requires London or Geneva office presence three to four days weekly and international customer travel.

Build and maintain investment data solutions, quantitative models, performance attribution systems, dashboards, and automated reporting workflows. Partner with investment, technology, and risk teams to support portfolio construction, research, manager selection, and investment oversight. Responsibilities include validating data, improving data quality, applying model governance, conducting quantitative analysis, and evaluating emerging technologies such as artificial intelligence and machine learning.

Lead product data science analyses across user behavior and content data to guide product and strategy decisions. Design evaluation frameworks for content classification, taxonomy, safety, and annotation-based measurement. Build scalable metrics, dashboards, reliable analytical datasets, self-serve tools, and data agents. Partner with Product, Engineering, and Operations leaders, while providing methodological leadership, mentorship, and analytical standards across the team.

Support Traded Risk Management by delivering high-quality market data solutions: develop derived market data and proxy models, analyse large datasets, coordinate market data requirements and delivery, validate daily market data, liaise with stakeholders across Risk, Trading, Model Validation and IT, and support regulatory and strategic data programmes.

Build, maintain, research, and improve credit risk and customer lifetime value models used for lending decisions, pricing, and portfolio strategy. The role includes production model health, incremental development, causal estimation, IFRS accounting models, and applied R&D. You will own ambiguous analytical problems end to end, use AI for prototyping and automation, critically evaluate model outputs, and communicate technical conclusions clearly.

Own credit risk and Customer Lifetime Value modeling projects end to end, including research, production deployment, monitoring, and commercial impact. Set technical direction across multi-quarter initiatives, evaluate uncertainty and model calibration, improve existing models, and explore new machine learning approaches. Lead projects, communicate clearly, challenge assumptions with evidence, and use AI to accelerate prototyping and R&D.

Manage data governance across Retail Products by defining critical data assets, maintaining metadata repositories and data dictionaries, documenting lineage and quality rules, profiling data, and improving data accuracy. Build stakeholder relationships, promote data management best practices, develop value cases for metadata improvements, implement governance tools, and coach a small team of data professionals.