A Data Science Manager manages a team of data scientists, machine learning engineers and big data specialists. They lead data mining and collection procedures, ensure data quality and integrity, build analytic systems and predictive models, and test the performance of data science products.
Permanent Overview A large, complex enterprise is seeking a senior data science leader to build and scale its data science function. This role is responsible for translating data into predictive, prescriptive, and AI-enabled insights that improve customer experiences, operational performance, decision-making, and organisational outcomes. The Director, Data Science will lead teams focused on statistical modelling, machine learning, experimentation, forecasting, optimisation, decision support, and AI-enabled analytics. The position partners closely with data, engineering, product, digital, and business stakeholders to ensure analytical solutions are built on trusted data and deliver measurable value. This leader will establish best practices for model development, validation, deployment readiness, monitoring, and lifecycle management while fostering a high-performance, innovation-focused culture. Key Responsibilities Data Science Strategy & Leadership Define and execute the enterprise data science roadmap aligned to strategic business priorities. Identify opportunities where advanced analytics, machine learning, optimisation, and AI can improve organisational outcomes. Prioritise investments based on business value, feasibility, data readiness, and strategic importance. Establish standards for experimentation, model development, validation, documentation, monitoring, and governance. Ensure solutions are scalable, reusable, interpretable where appropriate, and aligned with enterprise architecture and responsible AI principles. Advanced Analytics, Machine Learning & AI Lead development of predictive, prescriptive, optimisation, and forecasting models across multiple business domains. Oversee customer segmentation, risk modelling, recommendations, personalisation, propensity modelling, and decision-support solutions. Partner with engineering and product teams to operationalise analytical capabilities within production environments and business workflows. Support AI initiatives by defining methodologies, evaluation approaches, and performance expectations. Promote modern statistical and machine learning techniques while ensuring appropriate application and risk management. Model Evaluation, Experimentation & Measurement Define frameworks to evaluate model performance, business impact, reliability, fairness, and adoption. Lead experimentation strategies including A/B testing, causal inference, impact measurement, and test design. Collaborate with engineering, product, quality, and business teams on model monitoring and continuous improvement. Translate analytical outputs into actionable insights and business recommendations. Establish reporting, dashboards, and executive-level performance reviews. Cross-Functional Collaboration Partner with enterprise data teams to ensure access to reliable, high-quality data. Collaborate with knowledge management, semantic modelling, and data product teams to improve analytical outcomes. Work closely with product, operations, technology, and business leaders to identify and prioritise analytical opportunities. Support production deployment of models and analytical services in partnership with engineering teams. Provide strategic guidance on where advanced analytics and AI can create value, and where alternative approaches may be more appropriate. Team Leadership & Development Build, lead, and mentor a high-performing team of data scientists and analytics professionals. Define organisational structure, hiring strategy, capability development, and career progression pathways. Establish effective collaboration models across data, engineering, governance, product, and business teams. Coach team members on technical excellence, stakeholder engagement, communication, and business impact. Foster a culture of innovation, accountability, continuous learning, and responsible AI. Required Qualifications Education Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, Operations Research, Public Health, Informatics, or a related field. Master's degree or PhD preferred. Experience 12-18+ years of experience in data science, machine learning, advanced analytics, applied statistics, or related disciplines. 5-8+ years leading data science, analytics, or machine learning teams. Proven experience delivering analytical solutions that influence business or operational decision-making. Experience partnering with engineering, technology, product, and executive stakeholders. Strong background in model development, experimentation, monitoring, and production deployment. Experience within a regulated industry preferred. Technical Expertise Deep expertise in statistical modelling, machine learning, predictive analytics, optimisation, experimentation, and decision science. Proficiency with Python, R, SQL, notebook-based development environments, and modern machine learning frameworks. Experience assessing data quality, feature readiness, model performance, bias, drift, interpretability, and operational fit. Understanding of model deployment, monitoring, integration, and lifecycle management within production environments. Familiarity with AI, Generative AI, large language model evaluation, recommendation systems, forecasting, personalisation, and decision-support methodologies. Strong knowledge of privacy, security, governance, and responsible AI practices. Preferred Qualifications Experience leading data science teams within healthcare, life sciences, financial services, or other highly regulated industries. Experience supporting AI-enabled products, digital experiences, operational optimisation, workflow automation, or decision-support capabilities. Experience building reusable analytical frameworks and platforms that support multiple business functions. Experience defining enterprise standards for model evaluation, observability, monitoring, and continuous improvement. Familiarity with semantic models, ontologies, knowledge graphs, or enterprise knowledge management frameworks. Strong executive communication and stakeholder management skills. CONTACT Michael DeVita Recruitment Consultant
