Top Data Jobs in London

Lead data science for Wise’s cross-border transfer pricing strategy. Design pricing experiments, apply causal inference, model price elasticity and revenue impacts, optimize prices across customer segments, and build automated reporting and data infrastructure. Use machine learning to analyze pricing-related customer contacts, identify customer pain points, and deliver insights to Product, Operations, Finance, Commercial, and senior leadership.

Lead software engineering initiatives for data platforms within an agile team. Provide technical guidance, develop and review secure production code, solve complex design and functionality problems, and influence application architecture and technical operations. Drive responsible adoption and governance of AI-assisted engineering tools, including code review, refactoring, testing, troubleshooting, and incident analysis. Serve as a subject matter expert, promote engineering standards, and contribute to firmwide software development practices.

Design, build, and operate enterprise-grade, cloud-native data platforms and pipelines for Preqin's Company Intelligence. Provide technical architecture, end-to-end delivery, data governance, observability, and reliability while leading and mentoring a small team and partnering with product and analytics to deliver measurable business value.

The Lead Value Engineer is a trusted advisor to customers, driving their value journey through analytics, process improvement, and stakeholder engagement, particularly in Finance and Supply Chain.

Own and operate production Python infrastructure for FX pricing, risk, and trading systems. Responsibilities include developing microservices, shared quantitative libraries, monitoring and alerting, CI/CD, deployment, incident response, and root-cause analysis. The role also offers growth into curve construction, pricing models, trading strategies, risk modeling, backtesting, and customer behavior modeling while collaborating with quants, traders, analysts, product managers, and engineers.

Design, implement, operate, and troubleshoot CrowdStrike’s global multi-vendor, multi-cloud, hybrid data center networks. Build cloud connectivity and network constructs, develop infrastructure-as-code and automation using Ansible, Terraform, Python, and Go, improve resilience and deployment efficiency, support incident analysis and on-call rotations, and ensure compliance with regional data sovereignty and security requirements.

The AI Portfolio Lead will develop AI product offerings, analyze market trends, manage curriculum, and ensure high-quality learning experiences while influencing policies and stakeholder engagement.

Supports data governance, quality, integrity, lineage, access controls, metadata, and regulatory compliance for critical data assets. Partners with product managers, engineers, architects, and business stakeholders to monitor controls, resolve data issues, maintain governance documentation, improve discoverability, and escalate risks across the data lifecycle.

Conduct exploratory data analysis to improve financial crime controls and transaction monitoring rules. Tune thresholds to balance detection accuracy with operational workload, support partner risk monitoring, and maintain financial crime key risk indicators and reporting. Perform deep dives into emerging risks, identify compliance gaps, and recommend measures to mitigate suspicious activity across Wise Platform. Collaborate with Product, Compliance, Fincrime, KYC, and Business Operations teams.

Build end-to-end data, analytics, machine learning, generative AI, and agent-based solutions. Develop predictive models, conduct experiments, analyze trends, create insights, and support governed, secure, scalable data platforms. Collaborate in agile teams while applying data management, privacy, risk controls, and compliance practices. The program includes mentorship and training for students pursuing quantitative or technical degrees.

Leads end-to-end data analytics projects supporting strategic commercial, operational, and financial decisions. Builds and governs Domo dashboards and reports, establishes data quality and governance standards, documents data lineage, and presents executive-ready insights. Partners with senior stakeholders across Product Operations, Commercial, Finance, and Leadership to prioritize analytics work, manage competing requests, and communicate trade-offs. Requires strong SQL, BI, data warehousing, ETL, stakeholder communication, and project ownership skills.

Analyze, clean, validate, and reconcile data from multiple sources; write SQL; maintain databases; build dashboards, reports, and KPIs; investigate discrepancies and integration failures; support migrations and data pipelines; and provide UK/Europe clients with data-related support. The role also requires documenting incidents, explaining technical findings to non-technical stakeholders, monitoring processing jobs, and collaborating with Product, Engineering, and Program Management.

Turn data into product and business decisions through metric definition, experimentation, causal analysis, deep-dive research, and clear recommendations. Model reliable analytical data in dbt, build scalable dashboards and reporting, partner with data engineering, and influence leaders across Product, Finance, Growth, GTM, and Customer Success. The role also helps establish data-team standards, tooling, and operating practices in an AI-first environment.

The Partner Program Manager will design and manage a tiered partner program, focusing on value-added systems for partner rewards, enablement, and performance analytics.

Leads research and production development of generative AI and large language model systems for cybersecurity. Responsibilities include defining AI research strategy, developing novel reasoning and summarization approaches, selecting model architectures and deployment strategies, building large-scale production systems, mentoring scientists, establishing evaluation standards, and guiding cross-functional technical initiatives. The role also represents the company through publications, speaking engagements, and technical thought leadership.

Lead the AML Risk data science team and develop machine learning, GenAI, and statistical solutions for global anti-money laundering detection. Build supervised and unsupervised detection controls, evaluate and optimize model performance, create investigator insights, improve operational processes, and deploy production-grade Python services and automated retraining pipelines. Responsibilities also include hiring, mentoring, cross-functional collaboration, and managing projects that improve model training and financial crime detection.

Leads end-to-end development and production deployment of machine learning, generative AI, and agentic solutions for payments and banking. Responsibilities include problem framing, experimentation, evaluation, MLOps, LLMOps, governance, monitoring, scalable cloud architecture, security, and systems integration. The role establishes reusable AI capabilities, partners with cross-functional stakeholders, mentors technical teams, and communicates model outcomes and operational tradeoffs to technical and non-technical audiences.

Owns end-to-end go-to-market strategy for Diligent Boards, including positioning, messaging, launches, market intelligence, sales enablement, pricing, packaging, and adoption. Partners with Product, Engineering, Sales, Customer Success, Finance, and leadership to translate complex SaaS, AI, data, and integration capabilities into customer value. Leads customer research, competitive analysis, AI product marketing, commercial execution, and KPI-driven optimization across SMB and mid-market segments.

Analyze paid social audiences and platform performance to identify bidding, budget, and testing opportunities. Design experiments, validate measurement signals, maintain dbt data pipelines, and build reliable BI dashboards. Develop analytical frameworks for budget optimization, investigate anomalies, and communicate insights, risks, and trade-offs to marketing stakeholders. Collaborate with platform, data science, marketing science, and analytics engineering teams while applying attribution, unit economics, incrementality, and marketing measurement expertise.

Analyze Wise Platform pricing, costs, and profitability to identify pricing improvements and growth opportunities. Investigate complex cost data, improve data quality and allocation methodologies, explain product economics, monitor financial performance, and support pricing model enhancements. The role requires advanced SQL, visualization, dbt data modeling, stakeholder influence, and collaboration across analytics, engineering, and business teams.

Generate actionable growth and financial insights through dataset curation, metric development, statistical modeling, and deep-dive analysis. Analyze company and regional growth drivers, customer segments and cohorts, and the financial impact of marketing and product initiatives. Build data visualizations and communicate findings to influence business decisions, while managing stakeholders and organizing work independently.

Leads the design, development, and support of enterprise data platforms and pipelines. Ingests, cleanses, models, and documents data from diverse sources into governed Kimball datasets for BI, machine learning, and AI. Oversees data engineers, ensures pipeline reliability and quality, applies DevOps and CI/CD practices, performs data analysis, and collaborates with stakeholders on requirements, governance, GDPR compliance, and continuous process improvement.

Own end-to-end regulatory reporting datasets across multiple jurisdictions, including regulatory data mapping, automated pipelines, visualizations, data quality controls, monitoring, and discrepancy resolution. Collaborate with finance, compliance, regulatory, product, and analytics teams to support licensing, audits, partnerships, and controlled growth. Use SQL, dbt, Snowflake, Airflow, visualization tools, and AI capabilities to deliver accurate, scalable reporting infrastructure.

Lead pricing analytics for Wise Platform by investigating cost data, improving data quality and allocation methodologies, monitoring financial performance, and identifying profitability and growth opportunities. The role requires building complex SQL data sources, developing reusable dbt models, explaining product economics, influencing pricing strategies, and partnering with analytics, engineering, and business teams.

Applies advanced statistical and machine learning methods to B2B supply optimization, builds datasets and models, designs experiments, creates visualizations and dashboards, and translates analysis into business recommendations. Leads analytical workstreams, automates reporting, documents reproducible code, communicates with senior stakeholders, and mentors junior analysts.