Senior Data Scientist

• 5+ years of experience working as a Data Scientist, Machine Learning Engineer, Applied Scientist, or similar role.
• Proven experience delivering machine learning and analytical solutions from discovery through to deployment and business impact.
• Advanced proficiency in Python and relevant data science libraries, including NumPy, PySpark, Scikit-learn, TensorFlow, and/or PyTorch.
• Strong experience using Databricks, including Jobs, Asset Bundles, Delta Lake, and MLflow.
• Experience working with business stakeholders to define problems, scope solutions, and communicate outcomes effectively.
• Experience mentoring data scientists or providing technical leadership within a cross-functional team.
• Bachelor's degree or higher in Statistics, Mathematics, Computer Science, Data Science, Engineering, or a related quantitative discipline.

• Strong statistical, analytical, and problem-solving skills, with the ability to turn complex data into actionable business insights.
• Expertise in machine learning and predictive modelling, with experience delivering solutions in a production environment.
• Ability to evaluate and refine models using appropriate experimentation, performance metrics, and business outcomes.
• Strong stakeholder management and communication skills, capable of translating technical concepts for non-technical audiences.
• Ability to operate independently, navigate ambiguity, and prioritise work to maximise business impact.
• Experience mentoring and supporting the development of other data scientists.
• Collaborative mindset with the ability to work effectively across Product, Engineering, Commercial, and Data teams.

Data sits at the heart of the company. This role ensures that Awin can fully leverage the data available across the group, enabling the development of advanced analytical and machine learning capabilities that support stronger commercial decision-making and enhance products used by client-facing teams.

As a Senior Data Scientist, you will apply advanced statistical techniques, machine learning methods, and analytical approaches to solve complex business problems and deliver actionable insights that guide business strategy. Working across areas such as forecasting, recommendation systems, network intelligence, experimentation, and predictive modelling, you will lead the design, development, evaluation, and deployment of scalable solutions that create measurable business impact.

You will work closely with business stakeholders, Product, Engineering, and Data teams to identify opportunities, shape problem definitions, and translate business challenges into practical technical solutions. You will be responsible for defining success metrics, establishing evaluation frameworks, and ensuring analytical solutions remain reliable, scalable, and aligned to business objectives.

In this role, you will lead the development of end-to-end feature pipelines, models, and MVPs, balancing rapid experimentation with long-term sustainability. You will also provide technical leadership to other data scientists, contribute to best practices across the team, and help foster a collaborative, data-driven culture within the organisation.

• Lead the development and deployment of machine learning, forecasting, recommendation, and analytical solutions that create measurable business impact.
• Partner with stakeholders across Product, Commercial, Engineering, and Data teams to identify opportunities and translate business challenges into data-driven solutions.
• Design and implement robust evaluation frameworks, experiments, and success metrics to measure performance and business outcomes.
• Apply statistical modelling, machine learning, and advanced analytical techniques to solve complex business problems and generate actionable insights.
• Build scalable feature engineering, model development, and deployment workflows in collaboration with Data Engineering and Software Engineering teams.
• Drive rapid MVP development, validating ideas quickly while ensuring a path to sustainable production solutions.
• Define and promote best practices for model development, evaluation, monitoring, and governance.
• Mentor and support other data scientists, contributing to technical capability and knowledge sharing across the team.
• Communicate findings, recommendations, and technical concepts clearly to both technical and non-technical audiences.
• Contribute to the evolution of the Data Science roadmap, identifying new opportunities to apply advanced analytics and machine learning.