Data Science Manager

About the Opportunity

Our confidential client is a technology-driven organisation operating in a high-urgency, customer-facing environment where reliability, scale, security, and data-informed decision-making are critical.

We are seeking a hands‑on Data Science Manager to lead the development of analytical and machine‑learning capabilities that support business growth, operational performance, customer experience, and strategic decision-making.

This role requires a leader who can translate complex business problems into practical, measurable data solutions; develop a capable team; and partner closely with Product, Engineering, Operations, Technology, and business stakeholders.

Key Responsibilities

• Lead, mentor, and develop a high‑performing data science and analytics team.
• Define and execute a practical data science roadmap aligned with business priorities and measurable outcomes.
• Partner with Product, Engineering, Operations, Technology, and commercial stakeholders to identify high‑impact data opportunities.
• Translate ambiguous business questions into structured analytics, experimentation, forecasting, optimisation, segmentation, predictive‑modelling, or machine‑learning initiatives.
• Oversee the end‑to‑end data science lifecycle: problem framing, data exploration, feature development, model design, validation, deployment, monitoring, and continuous improvement.
• Establish standards for data quality, experimentation, statistical rigour, model validation, documentation, reproducibility, and responsible use of data.
• Ensure models and analytical products are reliable, scalable, explainable where appropriate, and fit for production environments.
• Work with Data Engineering and Software Engineering teams to improve data accessibility, data pipelines, model deployment, and model monitoring.
• Communicate insights, recommendations, risks, and model performance clearly to senior leaders and non‑technical stakeholders.
• Drive adoption of data‑informed decision‑making across relevant business and operational functions.
• Define success metrics for data science initiatives, including business impact, model performance, operational adoption, and return on investment.
• Contribute to data governance, privacy, security, and model‑risk practices.
• Support team planning, recruitment, capability development, and the continuing maturity of the data science function.

Qualifications and Experience

• Bachelor’s degree or higher in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Physics, or another relevant quantitative discipline.
• Proven experience leading data science, advanced analytics, machine learning, or decision‑science initiatives.
• Demonstrated people‑leadership experience, including coaching, developing, and managing data science or analytics professionals.
• Strong practical understanding of statistical analysis, experimental design, predictive modelling, machine learning, and data storytelling.
• Strong proficiency in Python and SQL.
• Experience working with large, complex, and production‑relevant data sets.
• Experience translating business needs into analytical and machine‑learning solutions with measurable impact.
• Experience working with cross‑functional stakeholders in a fast‑paced technology or operational environment.
• Strong commercial judgement to prioritise data initiatives based on business value, feasibility, data readiness, risk, and expected impact.
• Clear communication skills, including explaining technical findings to technical and non‑technical stakeholders.
• Professional English communication skills.

Preferred Qualifications

• Experience with cloud data and machine‑learning platforms such as AWS, Google Cloud, Azure, Databricks, Snowflake, or equivalent environments.
• Experience with MLOps practices, including model versioning, experiment tracking, CI/CD, monitoring, and model governance.
• Experience in a large‑scale digital, platform, fintech, e‑commerce, marketplace, logistics, mobility, or other technology‑driven environment.
• Experience with customer analytics, risk, fraud, personalisation, recommendation, optimisation, operational intelligence, or other data‑intensive use cases.