Data Science Manager

JMAN is the commercial data partner that specializes in maximizing value creation activities for private equity funds and their portfolio companies. We partner with our clients to address the growing need for investment decisions and value creation initiatives to be backed by reliable, real-time data. When companies partner with JMAN, we combine our data science and data engineering expertise with our deep commercial understanding to deliver tangible, high-value outcomes at pace. Founded in 2010, JMAN has a global footprint with offices in New York, London and Chennai. Our team of more than 350 experts partner with more than 80 private equity funds and over 200 portfolio companies. Nearly 85% of our business is from recurring partnerships with our clients. JMAN has been a portfolio company of Baird Capital since 2023.

We are a rapidly scaling business focused on delivering applied data science solutions that create measurable commercial value. While we work across a range of AI paradigms, data science sits at the core of what we do, and we are looking for individuals with deep, proven expertise in this area.

This is a technical delivery leadership role for a highly experienced Data Scientist who leads with structured problem solving and client empathy. The primary measure of success is whether clients achieve meaningful business outcomes through robust, well-designed data science solutions, not the adoption of the latest AI trends.

You will own the full lifecycle of data science engagements: shaping ambiguous problems, designing and delivering statistical and machine learning models, and ensuring they are embedded into operational workflows. You will work across organisations at every stage of data maturity, applying sound judgement to determine what is feasible, practical, and commercially valuable.

While the role may involve exposure to Generative AI and autonomous AI, these are not substitutes for strong data science fundamentals. Candidates must demonstrate deep, hands-on experience delivering applied machine learning and statistical modelling in real-world settings.

Internally, you will act as a data science standard-setter, helping define best practice, raising the quality bar, and developing others within the team.

Scope of Work

Our work spans multiple AI paradigms, with a clear emphasis on Data Science as the primary discipline for this role:

• Data Science (Primary Focus):
  
• Statistical modelling, machine learning, predictive analytics, and decision science delivered into production environments and real business processes.
• Generative AI (Secondary):
  
• LLM-powered applications such as RAG pipelines and document intelligence, applied where appropriate to support data-led solutions.
• Autonomous AI (Emerging):
  
• Agent-based systems and AI workflows, used selectively where they add clear value.

~75–80% of the role is focused on Data Science-led delivery, including problem structuring, model development, evaluation, and operationalisation. The remaining time is spent on client engagement, team leadership, and broader AI exposure where relevant.

Core Responsibilities

• Own end-to-end delivery of data science workstreams, from problem definition through to production deployment and ongoing monitoring.
• Translate ambiguous business problems into hypothesis-driven data science solutions, grounded in statistical rigour and practical feasibility.
• Lead the design and deployment of machine learning models, ensuring they are robust, explainable, and operationally viable.
• Provide technical oversight across projects, with a strong focus on model quality, evaluation frameworks, and reproducibility.
• Work closely with data engineers to ensure models are effectively integrated into production systems, with appropriate pipelines, monitoring, and lifecycle management.
• Apply sound judgement in selecting modelling approaches, balancing sophistication with interpretability, maintainability, and business value.
• Act as a trusted advisor to clients, communicating complex data science concepts clearly and influencing decision-making at senior levels.
• Manage delivery timelines, risks, and priorities across engagements.
• Set the standard for data science excellence, including coding standards, documentation, and best practices.
• Mentor junior team members, supporting their development as data scientists.

The most important requirement for this role is proven, hands-on experience in Data Science. Candidates must be able to demonstrate a strong track record of delivering end-to-end machine learning or statistical solutions in real-world environments.

• 5+ years’ experience in Data Science or applied machine learning, ideally in consulting, high-growth, or commercial environments.
• Strong applied machine learning expertise, including:
  • Model selection and development
  • Feature engineering
  • Evaluation and validation
  • Deployment into production environments
• Demonstrated experience taking models beyond experimentation into operational, business-critical systems.
• Strong background in statistics and quantitative problem solving.
• Proficiency in Python and SQL, with experience writing production-quality, maintainable code.
• Experience with modern data science tooling, including:
  • Version control (Git)
  • Cloud platforms (AWS, GCP, or Azure)
  • Experiment tracking and model management
  • Workflow orchestration and containerisation
• Strong understanding of MLOps principles, including model monitoring, retraining, and lifecycle management.
• Proven ability to translate business problems into data science solutions that deliver measurable outcomes.

Beneficial

• Exposure to Generative AI (e.g. LLMs, RAG) is beneficial but not a substitute for core data science expertise.
• Awareness of emerging AI paradigms (e.g. agent-based systems) is a plus, but not required.

Specialisms (Data Science–led)

We particularly value depth in applied data science areas such as:

• Predictive Modelling & Forecasting
• Customer analytics (segmentation, churn, LTV)
• Pricing and optimisation models
• Time series modelling
• Experimental design and causal inference
• Natural Language Processing (within a traditional ML framework as well as LLM-based approaches)

If you feel that you would be a strong addition to our team, but you do not fully meet all the requirements above, we would like to encourage you to please apply anyway. As we expand, we are looking for individuals across all levels and maybe able to discuss a suitable alternative with you.

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.

Other information

• 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
• Hybrid working - minimum of 3 days in the office (E3N)