Top Data Science Jobs in Manchester

Own enterprise customer programs from contract signature through expansion, leading delivery teams, milestones, stakeholder communication, adoption, and change management. Partner with sales and customer success to scope engagements, identify expansion opportunities, and translate executive goals into executable AI implementation plans. Serve as the customer advocate internally while guiding solutioning across model development, deployment, governance, and enterprise data science workflows.

Graduate analysts rotate across Architecture, Engineering and DevOps, Data Science, and AI while delivering data-driven technology solutions for clients. Responsibilities include designing and developing software, supporting AI-enabled solutions, collaborating with multidisciplinary teams, creating technical designs, and working with testing and DevOps specialists on automated releases. The programme provides mentoring, technical training, consulting experience, and exposure to cloud platforms, software engineering, and modern data practices.

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.

Leads Ofcom’s research innovation and data strategy, overseeing statistics, analytics, behavioural research, innovative research methods, and programme management. The role develops responsible data and AI capabilities, improves access to insight, strengthens cross-organisational collaboration, manages senior teams and budgets, and represents Ofcom externally. It requires strategic leadership, advanced data science and research expertise, data ethics knowledge, programme management, change leadership, and the ability to communicate complex analysis to regulatory, policy, commercial, and external stakeholders.

Lead the transformation of legacy products into a unified enterprise platform. Translate strategy into roadmaps, manage backlogs, prioritize cross-functional initiatives, and coordinate Engineering, UX, AI, Data Science, Commercial, and Operations teams. Drive platform discovery, delivery, launch, adoption, and measurable business outcomes. Integrate trustworthy AI and data capabilities into customer-facing workflows while influencing senior stakeholders and balancing customer value, commercial goals, technical feasibility, risk, and resources.

Develops Gartner research and thought leadership on the economic impact of AI and technology disruption. Synthesizes economic analysis, quantitative evidence, financial statements, market intelligence, and business context into executive-level insights. Advises clients, presents at conferences and media engagements, supports business development, reviews research, and collaborates with modeling, data science, and data engineering teams. The role also contributes to research strategy, mentors colleagues, and builds authority in technology market economics.

Leads automation strategy for global VAT compliance and reporting. Identifies process inefficiencies, translates VAT requirements into technical solutions, manages automation projects through implementation, and supports ongoing process improvements. Works with clients, VAT teams, delivery groups, and technology teams while applying analytics, data science, AI, workflow automation, and emerging technologies. Also coaches team members, manages delivery risks, ensures data integrity and controls, and prioritizes initiatives independently.

Participate in a 24-month rotational engineering analyst program focused on data science. Apply data analysis, profiling, feature engineering, statistical methods, and machine learning to solve business and engineering problems. Build scalable analytical processes, models, and tools using Python, JavaScript, Java, CSS, and related technologies. Collaborate with engineering and business teams to translate data insights into practical recommendations and improve decision-making.

Design and develop AI solutions for complex problems, integrating AI capabilities into systems and improving effectiveness through experimentation and evaluation.

Develop and evaluate AI/ML and Generative AI/LLM models from POC to production, perform data science and statistical analyses to support business decisions, document and assess model risk, publish patents/papers, collaborate with SMEs, and provide technical leadership while deploying solutions with modern Agile/DevOps and cloud practices.

Leads the Payments data strategy and partners with Product, Engineering, and Design to shape payment products, commercial optimization, customer growth, metrics, architecture, and operating models. Builds and scales a high-performing, full-stack data organization spanning data science, analytics engineering, machine learning engineering, and analytics. Serves as a trusted Payments Leadership partner, translating technical, product, and commercial perspectives into high-impact decisions.

Leads a 25+ person organization of data scientists, machine learning engineers, and analytics engineers across Identity and Financial Crime. Owns data quality, governance, lineage, observability, fraud and financial-crime models, production performance, monitoring, retraining, and domain data products. Partners with engineering, product, risk, compliance, legal, and central data teams; influences upstream teams, develops managers and senior ICs, handles incidents, and scales hiring and technical strategy.

Leads and develops a team of Data Scientists and other Data professionals, setting strategic direction and identifying high-impact opportunities. Partners with Product, Engineering, and senior stakeholders to shape product strategy and influence decisions through experimentation, causal inference, statistical modelling, machine learning, and analytics. Ensures analytical work drives measurable customer and commercial outcomes while collaborating with other data disciplines on tooling and infrastructure. Also contributes to hiring, coaching, technical standards, and the broader Data Science practice.

Lead a financial crime data science team focused on detecting, preventing, and mitigating financial crime. Partner with Product, Risk, Engineering, and control owners to shape data strategy, generate strategic insights, improve product decisions, and deliver rigorous analytics and machine learning solutions. Hire, develop, and retain data scientists and analysts while supporting innovation through business intelligence, experimentation, causal inference, and self-serve analytics.

Lead technical discovery, solution design, demonstrations, and proof-of-concept support for Anaconda’s AI and data science platform across EMEA. Partner with account executives on complex deals, create reusable technical content, represent customer needs to product and engineering, and coordinate with cross-functional teams through implementation handoff. Manage multiple opportunities independently while improving technical win rates, deal expansion, sales velocity, and regional pipeline development.

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.

Develop and scale NVIDIA Inception’s European startup ecosystem by identifying, technically assessing, recruiting, enabling, and nurturing AI and accelerated-computing startups. The role advises founders and technical leaders on CUDA, NVIDIA SDKs, AI frameworks, GPU infrastructure, and platform adoption; leads technical workshops; translates ecosystem feedback into product insights; collaborates with venture capital firms and internal teams; and supports sales and enterprise customer motions. Travel across Europe is required approximately 40% of the time.

Build Paddle’s data science function and production decisioning systems from the ground up. Prioritize high-value use cases, personally deliver the first ML or agentic system, and own deployment, monitoring, experimentation, reliability, governance, and financial impact. The role will establish the production ML stack, collaborate across Product, Engineering, Risk, Finance, and Compliance, then hire and lead data scientists and ML engineers. Experience with regulated transactional domains, production ML operations, experimentation, and executive stakeholder management is required.

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.

This role invites speculative applications for various tech positions. Candidates with skills in Salesforce, DevOps, AWS, and user experience are encouraged to apply.

Participate in a 10-week data science engineering internship by applying analytical methods to business and engineering problems. Analyze and profile data, perform feature engineering, identify patterns and anomalies, and build automated models and analytical tools. Collaborate with engineering and business teams to translate insights into recommendations and scalable data-driven solutions. Gain experience in programming, advanced analytics, machine learning, distributed computing, and communicating complex results.

The Senior AI Transformation Consultant works with clients to identify AI opportunities, define robust business cases, and oversee AI project deployments, ensuring solutions deliver value.

Manage curriculum development for data science and engineering, collaborate with instructors, create content, and drive improvements based on learner feedback.

As a Curriculum Manager, you will manage content development for the data science and engineering curriculum, collaborating with instructors and teams to enhance learning experiences.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.