JMAN Group is a fast-growing data engineering & data science consultancy. We work primarily with Private Equity Funds and their Portfolio Companies to create commercial value using Data & Artificial Intelligence. In addition, we also work with growth businesses, large corporates, multinationals, and charities.
We are headquartered in London with Offices in New York, London and Chennai. Our team of over 600 people is a unique blend of individuals with skills across commercial consulting, data science and software engineering.
The role
We’re a rapidly scaling business at the forefront of applied AI delivery. We move fast, operate with ambiguity, and hold ourselves to a high standard. We’re looking for people who thrive in exactly that environment.
This is a technical delivery leadership role for someone who leads with creative problem solving and client empathy, using AI as the means, not the end. The measure of success is whether clients achieve meaningful business outcomes, not whether the most sophisticated technology was deployed.
You will own the full lifecycle of AI engagements: shaping ambiguous problems, making pragmatic solution choices, managing delivery, and sustaining client relationships across organisations at every stage of data maturity. The role demands as much emotional intelligence as technical ability, knowing when to push, when to simplify, and how to bring clients with you through complexity.
Internally, you will be a standard‑setter, someone others look to for how great work gets done, how clients get managed, and how the practice continues to evolve. You will help shape how we work, contribute to our growth, and invest in the people around you.
The scope of AI solutions spans three paradigms:
- Data Science: statistical modelling, machine learning, and predictive analytics delivered into operational workflows.
- Generative AI: LLM‑powered solutions including RAG pipelines, document intelligence, and prompt‑driven automation.
- Autonomous AI: agent‑based systems and AI‑driven workflows that act, reason, and execute with limited human‑in‑the‑loop.
The core of the role (70–75%) is technical delivery leadership: structuring problems, making sound solution decisions, and ensuring outputs are reproducible, explainable, and built to operate reliably in real environments. The remaining 25–30% is engagement management: owning the client relationship, driving quality, supporting commercial growth, and developing junior team members.
Core Responsibilities
- Own end‑to‑end delivery of AI workstreams across DS, GenAI, and autonomous AI, from scoping and feasibility through development and operationalisation.
- Lead with the client’s business problem, not the technology. Define what success looks like in outcome terms before selecting a solution approach.
- Shape ambiguous problems into hypothesis‑led, testable objectives. Select solution types pragmatically based on what will actually work given client context and constraints.
- Provide technical oversight to cross‑functional delivery teams; review solution design, evaluation approaches, and deployment architecture.
- Work closely with data engineers and architects to ensure solutions are designed to operate reliably at scale, accounting for pipeline dependencies, platform constraints, and long‑term maintainability.
- Adapt technical approach and communication style to clients across all levels of the data maturity curve, from building foundational capability to running sophisticated AI programmes at scale.
- Own the day‑to‑day client relationship up to c‑suite. Build trust through clarity, consistency, and sound judgement, not technical complexity.
- Identify and pursue upsell and cross‑sell opportunities; work with principals to extend engagements and deepen client relationships.
- Manage delivery milestones, surface risks early, and adapt plans as requirements evolve.
- Set the standard for quality and professional conduct across delivery teams. Lead by example in how you work, communicate, and solve problems.
- Contribute to internal initiatives: identify where the practice can improve, bring ideas to the table, and take ownership of making them happen.
- Build internal AI capability; contribute to frameworks, ways of working, and knowledge that raise the bar across the team.
- Support the growth and development of junior team members; invest in their progress with the same care you bring to client delivery.
Requirements
The most important qualities for this role are creative problem solving, the emotional intelligence to build strong client relationships, and a genuine focus on business outcomes over technology for its own sake.
We welcome applications from candidates with backgrounds in private equity, management consulting, high‑growth businesses, or start‑ups, anywhere that has demanded rigour, adaptability, and the ability to deliver under pressure with limited resource.
We’re open to candidates who have arrived at this skill set through non‑linear routes. What matters is that you can demonstrate technical depth and practical judgement comparable to an experienced data scientist, alongside the commercial instinct and leadership presence this role demands. Where you built those capabilities is less important than how well you can apply them.
- Current right to work in the UK.
- 5+ years’ experience in data science, AI, or technical consulting.
- A clear orientation toward solution‑focused thinking; able to distinguish between what a client is asking for, what they actually need, and what will work given their constraints.
- Strong interpersonal skills and emotional intelligence; builds trust across seniority levels, reads situations accurately, and adapts communication style to the audience.
- Proven track record of end‑to‑end delivery, from problem definition through to operationalisation, with accountability for outcomes, not just outputs.
- Solid applied machine learning competence: a track record of taking ML models beyond experimentation into reliable, operationalised systems, with sound judgement on model selection, feature engineering, evaluation, and lifecycle management.
- Experience writing and owning code that is reproducible, maintainable, and built to be understood, extended, and operated by others.
- Proficiency in Python and SQL, with hands‑on experience across relevant DS/AI tooling including version control (Git), cloud infrastructure (AWS, GCP, or Azure), experiment tracking, containerisation, and workflow orchestration.
- Depth in at least one AI paradigm (DS, GenAI, or autonomous AI) and working knowledge of at least one other; breadth across all three is strongly valued.
- Ability to work closely with data engineers and architects; understands platform constraints, pipeline design, and how solution choices affect downstream systems.
Specialisms
While hypothesis‑driven applied ML is our primary focus, the following specialisms represent areas of growing importance across our client engagements. Candidates with demonstrable depth in one or more of these areas would be a particularly strong fit.
- NLP: hands‑on experience with text‑based AI solutions including entity extraction, classification, summarisation, semantic search, or document understanding, delivered in real operational settings.
- Generative AI: practical experience designing and delivering GenAI solutions at the application layer, including RAG architectures, prompt engineering at scale, LLM evaluation, fine‑tuning, and integrating foundation models into business workflows.
- Deep Learning: applied experience designing, training, and deploying neural network architectures across domains such as computer vision, sequential data, or multimodal inputs, with an understanding.
- Breadth across all three AI paradigms; able to select and blend DS, GenAI, and agentic approaches based on problem structure, data constraints, and commercial context.
- Experience with autonomous AI systems: agent orchestration, tool use, multi‑step reasoning pipelines, and guardrail design.
- Awareness of MLOps/LLMOps practices: drift detection, live monitoring, model versioning, retraining cadence, and rollback strategies.
- Involvement in sales or commercial processes, supporting or leading conversion of a technical opportunity into a delivered engagement.
- Experience working in a cross‑functional, international delivery model.
Ways of working
- Leads with the problem, not the solution.
- Sets a high standard and brings others with them.
- Proactive and improvement‑minded.
- Resilient and focused under pressure.
- Brings ownership and initiative to every engagement.
- Technically credible and commercially aware.
- Collaborative and well‑organised.
- Committed to their own development and genuinely invested in the growth of the people around them.
- Comfortable sharing a perspective, questioning an approach, or proposing a new direction constructively.
What we offer:
- Hybrid working – minimum of 3 days in the office (EC3N)
- Discretionary bonus – based on personal and company performance
- 25 days annual leave + bank holidays
- Pension with a company contribution up to 8%
- Health Insurance from day 1
- Life insurance and long‑term disability insurance
- Market‑leading parental leave policy
- Salary Sacrifice Nursery Scheme through YellowNest
- Salary Sacrifice EV Scheme with Octopus Vehicles
- Additional Health Cash Plan with MediCash
- Cycle to work scheme
- Referral bonus for bringing in new JMAN hires
- Extensive training and coaching opportunities
- Regular company socials and retreats
JMAN is committed to equal employment opportunities. We are a diverse, high performing team and base all our employment decisions on merit, job requirements and business needs.
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