Data Science Manager

Job summary

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.

Qualifications

• This is a technical delivery leadership role for a highly experienced Data Scientist who leads with structured problem solving and client empathy.
• Candidates must demonstrate deep, hands-on experience delivering applied machine learning and statistical modelling in real-world settings.
• 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 developmentFeature engineeringEvaluation and validationDeployment 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 managementWorkflow 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.

Responsibilities

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

Skills

Degrees

Industry

Company size