Top Machine Learning Jobs in Manchester

Drives full-cycle sales of Data and AI solutions, including data readiness assessments, modern data platforms, governance, analytics, applied AI, and managed services. Builds pipeline, leads customer discovery, develops proposals and business cases, negotiates deals, and achieves revenue targets. Partners with account teams, Microsoft, and other vendors to deliver transformation opportunities. Advises senior technology stakeholders on data readiness, regulatory considerations, AI adoption, and multi-year commercial roadmaps.

Leads CDW’s UK Data & AI Solution Sales practice, defining strategy, propositions, go-to-market plans, and profitable growth. Builds data-readiness and AI-adoption offerings, manages senior vendor and customer relationships, drives revenue and partner programs, and ensures responsible, compliant delivery. Develops teams, certifications, forecasting, staffing, operational standards, and cross-functional collaboration across sales, security, cloud, delivery, marketing, legal, and compliance.

Lead the development and deployment of probabilistic and machine learning models powering real-time sports betting markets. Analyze large datasets, validate models against historical and live data, optimize accuracy and scalability, collaborate with trading and software teams, research novel techniques, and mentor junior data scientists.

Develop machine learning and statistical models, analyze large datasets, conduct exploratory analysis and controlled experiments, and deliver actionable insights for sports and gaming products. The role involves data sourcing and validation, scalable data science infrastructure, stakeholder collaboration, and communicating findings through visualizations, reports, and presentations.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

Software engineering intern supporting the Platform Engines group and Policy Engine for SailPoint’s cloud identity security platform. Responsibilities include developing software, procedures, and test solutions; collaborating with software and QA engineers, technical writers, and product managers; and working in Agile teams. The intern will gain exposure to cloud and software technologies including Golang, Angular, AWS, Docker, Kafka, Kubernetes, REST, SQL, and Temporal.

Supports technical sales for HPC and AI solutions by gathering customer requirements, designing architectures, preparing proposals, delivering demonstrations and proof-of-concepts, and validating solutions. Collaborates with sales teams, customers, partners, and technical specialists to position solutions, manage opportunities, contribute to pipeline growth, and support successful knowledge transfer. The role also involves tracking emerging technologies, participating in forecasts, documenting activities, and developing expertise in enterprise infrastructure and cloud technologies.

Leads digital delivery and optimisation across AECOM’s England and Wales water operations. Develops digital strategies, advises clients on transformation, data governance, platforms and roadmaps, and champions AI, machine learning and data science adoption. Identifies automation opportunities, supports MVP development, manages regional enablement, contributes to bids, and facilitates client workshops and stakeholder presentations. The role requires strong digital expertise, infrastructure or water-sector experience, and the ability to influence technical and non-technical stakeholders.

Designs and integrates machine learning capabilities into game editors, engine tools, and game systems. Owns complex technical features, improves development pipelines and workflows, identifies technical risks, and delivers scalable, reliable solutions. The role also includes reviewing designs, mentoring engineers, conducting code reviews, and collaborating across disciplines to advance AI-enabled game development.

Leads data science, machine learning, and AI projects for customer service operations. Builds predictive models and actionable insights using SQL, Python, DAX, R, and Power BI. Collaborates with analysts, engineers, and stakeholders on feature stores, visualization, and business transformation. Coaches peers, presents findings, applies DataOps and MLOps practices, and translates business goals into technical requirements.

Analyzes fraud and financial crime performance for a virtual credit card, producing AML and fraud reporting, monitoring losses and alerts, optimizing rules and thresholds, and supporting regulatory and card-scheme compliance. The role conducts transaction analytics, validates new fraud solutions, investigates incidents, manages control enhancements, and partners with Product, IT, Operations, Compliance, and payment schemes to improve fraud controls and support safe product launch and ongoing performance.

Translate ambiguous business questions into actionable insights using Python, SQL, statistics, forecasting, regression, classification, segmentation, and machine learning. Analyze complex datasets, validate findings, build models, and communicate commercial implications to senior stakeholders. The role requires strong requirements gathering, stakeholder management, written communication, and the ability to turn analysis into practical decisions in a financial services environment.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Lead real-world evidence biostatistical analyses using large-scale healthcare data to support clinical development, regulatory strategy, HEOR, and payer decisions. Design observational studies and target trial emulations, apply causal inference, statistical learning, machine learning, and NLP, and build reproducible Python/R workflows. Collaborate with clinical, epidemiology, data engineering, and HEOR teams while translating findings for technical and non-technical stakeholders. Support regulatory submissions, governance, audits, and scientific dissemination.

Leads economists, econometricians, and data analysts delivering applied economic, econometric, machine learning, and policy analysis for communications regulation. Responsibilities include directing concurrent projects, evaluating policies, shaping regulatory decisions, communicating findings to stakeholders, producing publications, and mentoring colleagues. The role requires extensive real-world economic analysis experience, strong applied microeconomics and econometrics expertise, analytical software proficiency, and the ability to lead teams and develop innovative quantitative approaches.

Leads the design, development, deployment, and governance of scalable Data and AI solutions using Databricks, Microsoft Foundry, and Azure. Defines AI technology roadmaps, mentors engineering teams, identifies high-value business use cases, prototypes and scales AI products, and ensures secure, ethical, enterprise-integrated implementations.

As a Principal Engineer at M&S, you'll drive technical strategy, influence architecture, and mentor engineers while delivering on personalization, search, and machine learning features in collaboration with cross-functional teams.

Supports enterprise sales by discovering customer requirements, mapping them to SailPoint solutions, and delivering foundational implementations and proof-of-value engagements. Acts as a technical lead and consultative advisor, educating customers on architecture and best practices. Collaborates with sales, product, professional services, partners, and customer success teams while developing AI-driven and agentic solutions for demonstrations and customer outcomes.

Provides actuarial pricing and analytics support for Bupa’s UK insurance products. Responsibilities include analyzing portfolio performance, developing pricing recommendations, building models, evaluating customer and competitor trends, supporting product changes, interpreting results, and preparing reports for senior stakeholders. The role involves data extraction and refinement, advanced analytical techniques, technical documentation, and collaboration with Sales, Finance, Product, Clinical Policy, and Healthcare Analytics teams.

Manage NVIDIA’s relationships with AI-native startups, scaleups, and labs. Identify customer needs, develop AI solution strategies, drive adoption of NVIDIA platforms, coordinate cross-functional and partner resources, create customer references and case studies, develop emerging use cases, and represent NVIDIA at industry events. The role requires strategic market analysis, AI business development expertise, ecosystem relationships, and strong communication skills.

Owns strategy and growth for a defined market segment within CluePoints’ clinical-trial SaaS portfolio. Responsibilities include developing go-to-market strategies, coordinating product, marketing, sales, consulting, customer success, and finance teams, enabling teams to launch and support products, advising strategic customers, and leading user communities. Success is measured through market penetration, win rate, retention, NPS, and gross margin.

Own and build the company’s metadata-driven data layer as a hands-on principal architect. Design schema-flexible and versioned platforms with lineage, auditability, semantic mapping, and resilience to schema changes. Write production code, establish AI-assisted engineering practices with verification, coach engineers, and communicate architecture to technical and business stakeholders. Experience with regulated data, multi-tenant SaaS, semantic technologies, or clinical data standards is valuable.

Own the strategy, roadmap, development lifecycle, and go-to-market activities for fraud analytics products. Lead customer discovery, prioritize product improvements, analyze complex data, collaborate with development and data science teams, and use market and customer insights to improve user outcomes. The role requires fraud prevention expertise, statistical modeling and AI/machine learning knowledge, strong stakeholder communication, and flexible UK-US working hours.

Serve as a customer-facing fraud and identity expert across pre-sales, onboarding, and expansion. Design risk strategies and identity workflows, advise customers on fraud mitigation, coordinate technical and regulatory implementation requirements, validate milestones, and lead go-live readiness. Partner cross-functionally with Product, Engineering, Legal, and Compliance teams to accelerate deployment, adoption, and customer value.