Top Data Jobs in London

Design, build, deploy, and optimize distributed big-data ingestion, standardization, and ML pipelines at scale. Support platform reliability, cost controls, security accreditations, and customer integrations. Serve as technical lead in client meetings, mentor engineers, and recommend tools and best practices for data pipeline development and deployment.

The Senior Data Scientist will unify technical vision, develop and deploy ML models, and enhance AI strategy across teams. Responsibilities include collaboration, ownership of projects, and maximizing value through AI and data science techniques.

Lead CRM analytics for Personal and Business segments: design measurement frameworks, run campaign and lifecycle analyses, apply A/B and incrementality testing, build segmentation models, maintain dashboards, ensure data quality, and partner with CRM, Product, and Data teams to optimise customer engagement and retention.

Maintain and enhance marketing attribution models and data pipelines (dbt), ensure data integrity, build dashboards for stakeholders, run in-depth analyses, and translate results into strategic recommendations to improve marketing effectiveness.

Serve as the primary data consultant for a business domain, translating stakeholder needs into prioritized data product requirements, conducting lightweight ad-hoc analysis (SQL, Looker), triaging requests, tracking business value and ROI, and enabling teams to self-serve while ensuring data quality, adoption and measurable impact.

The Staff Data Analyst leads complex projects, develops scalable data solutions, and mentors other analysts while driving strategic initiatives at Wise.

As a Data Management Consultant, you'll design and implement modern data management frameworks, support AI-enabled solutions, and drive adoption through stakeholder collaboration.

Lead a squad to design, develop, and operate the firmwide data catalogue (Fusion), driving secure, scalable solutions, AI-assisted engineering practices, automation, governance, and stakeholder collaboration to enable global data access and platform stability.

Lead growth & adoption analytics by building and maintaining agent-ready curated datasets, partnering with engineers and product teams, reviewing merge requests, defining pipelines and metrics, performing advanced SQL analyses on petabyte-scale data, and communicating actionable insights to stakeholders.

Lead delivery of business and technology transformation initiatives, managing plans, risks, governance, stakeholder communications, and coordinating Agile/Waterfall or hybrid teams while contributing to practice development and knowledge sharing.

Lead business development for DXC's insurance portfolio: originate and qualify pipeline, engage senior stakeholders, shape and close complex, high-value technology deals, collaborate with solution and delivery teams, and position AI-enabled and platform solutions to drive client transformation and expansion.

Support investment teams by acquiring, cleaning, and analyzing multi-source data to test investment theses, identify inflection points, and present research. Learn fundamental investor approaches, build decision-support tools, and communicate findings clearly to Sector Data Analysts and investment teams.

Lead and contribute to building scalable, production-grade data systems for private markets. Architect end-to-end data workflows supporting analytics and AI/ML, ensure observability, testing, and operational maturity, influence technical strategy, engage senior stakeholders, and manage and mentor software engineers while partnering with product, data science, and business teams.

Develop, implement, and deploy statistical and machine learning sports models and pricing engines in Python; engineer sports data assets; build automated tests; collaborate with Trading, Product, Engineering, and QA; validate data flows and integrations; and mentor junior data scientists.

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

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

Generate insights to inform company and customer decisions by curating datasets, surfacing metrics, performing statistical modelling, and deep-dive analysis. Analyze growth drivers, customer segments and cohorts, and link acquisition/activity to marketing and product initiatives to measure realised financial returns. Communicate findings through data visualisation and stakeholder management.

Own pricing model and P&L analysis for Wise Platform: investigate cost data, propose pricing and cost improvements, monitor financial performance, improve pricing model and liaise with subject matter experts to surface actionable insights that drive profitability and growth.

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.

Lead Data Scientist for CRM/personalisation owning end-to-end production ML: feature pipelines, model training, inference and impact measurement. Build predictive, uplift, and recommendation models, design and analyse A/B tests, partner with analysts, engineers and CRM managers, and translate models into measurable CRM growth levers.

Lead Data Scientist responsible for designing, implementing, and deploying liquidity management and risk models. Work with Treasury, engineering, and product teams to forecast supply and demand, optimise cash placement, prepare stress scenarios, and present model results to stakeholders to keep liquidity risk within appetite.

Lead a Treasury Global Liquidity data engineering team to design and deliver real-time liquidity observability, scalable event-driven systems, automated investment and risk tooling, and analytics/ML collaboration. Define technical strategy, partner with product and stakeholders, mentor engineers, and ensure resilient, high-performance data platforms that optimise cash management and customer experience.

Lead pricing analytics for Wise Platform: define and investigate pricing and cost problems, analyze product economics and P&L, improve data quality and allocation methodologies, influence pricing model enhancements, and surface actionable insights for profitability and growth.

Lead HR data privacy for workforce data across EMEA, overseeing DSARs, privacy incidents, DPIAs/LIAs, and ROPA. Partner with Legal, Compliance, Privacy, Cyber and Technology Controls to set guardrails, manage risk-based escalation, and advise senior stakeholders on regulatory compliance and data protection best practices.

The Client Partner will manage and grow strategic client relationships, primarily in the public sector, and drive account growth through strategic business development and client satisfaction.