The Role
You are a senior technical authority within the Data Science capability. You are responsible not only for building models, but for defining what should be built, ensuring quality, and bridging the gap between business outcomes and technical delivery.
In Phase 1, you will operate in a dual-mode environment: supporting the existing estate (Lane 1) while contributing to the build of a modern, governed data platform (Lane 2).
You do not spend most of your time cleaning data. Instead, you define the data requirements (features, structures, behaviours) needed for modelling, working closely with the Lead Data Modeller and Data Engineering teams to ensure the right data is built upstream.
Your responsibilities:
Hands on Model Development & Deployment
• Build and deploy predictive models and advanced analytics solutions for high-value use cases.
• Own the most complex and high-impact modelling problems.
• Ensure robustness, explainability, and performance of models in production.
Requirement Definition
• Define the features and datasets required for modelling.
• Collaborate with the Lead Data Modeller to shape semantic definitions and structures.
• Work with Engineering to ensure pipelines support modelling needs.
Business Partnership
• Translate business problems into analytical approaches.
• Challenge poorly defined requests and drive clarity on outcomes.
• Communicate trade-offs between model complexity, data availability, and delivery timelines.
Quality, Rigour & Standards
• Establish and enforce best practices for modelling, evaluation, and deployment.
• Implement peer review processes for models and code.
• Ensure ethical and responsible use of data and AI.
Mentorship & Leadership
• Mentor junior and mid-level Data Scientists.
• Support capability development and knowledge sharing.
• Act as a confident voice in shaping Data Science practices.
Cross-Discipline Collaboration
• Work closely with the Lead Data Modeller to define ‘Gold’ data required for analytics.
• Partner with Data Engineers to ensure model readiness and operational deployment.
• Align with Delivery Lead on prioritisation and feasibility.
Your Profile
EXPERIENCE AND QUALIFICATIONS
• Degree in a relevant field such as Computer Science, Mathematics or Engineering.
• At least 4 years of experience in a data science or statistical role with a proven track record of delivering impactful solutions.
• Experience in developing and deploying machine learning models and statistical analysis.
• Familiarity with distributed computing tools and cloud platforms (e.g., Azure Synapse, Snowflake, Databricks).
• Experience with data transformation and analytics tools (e.g., Python, SQL, Spark SQL).
• Proven ability to work with large datasets and complex data structures.
• Strong background in applying statistical and machine learning techniques to solve business problems.
SKILLS AND COMPETENCIES
• Technical skills - Proficiency in Python, R, SQL and machine learning frameworks with advanced knowledge of statistical techniques, data modelling, cloud platforms and data visualisation tools.
• Problem-solving and Analytical skills - Ability to translate complex data into actionable insights and develop predictive models for business solutions.
• Communication and Collaboration - Strong communication skills to convey technical concepts to non-technical stakeholders, collaborate across teams and mentor colleagues.
• Continuous Learning and Innovation - Stay updated on data science, AI, machine learning and emerging technologies.