Senior Data Scientist

Role & responsibilities

We are looking for a senior / principal level so they will have responsibilities for leading and advancing our data science initiatives. In this role, they will work at the forefront of machine learning and analytics, designing and deploying data-driven solutions that drive our business forward.

They will be responsible for developing predictive models, enhancing our ML infrastructure, and providing strategic insights to key stakeholders. This position requires advanced expertise in machine learning, a strong foundation in AWS and Python, and the ability to lead complex projects from ideation to deployment.

Key responsibilities

Machine Learning Model Development and Deployment

• Design, build, and deploy advanced machine learning models to solve complex business problems across various domains, such as predictive analytics, personalisation, anomaly detection, etc.
• Lead the end-to-end model lifecycle, including data preprocessing, feature engineering, model training, evaluation, and deployment.
• Use SageMaker and other AWS ML services to operationalise models, ensuring scalability, performance, and monitoring in production.
• Implement and enhance the ML infrastructure, including model pipelines, monitoring, and automated retraining processes.

Advanced Analytics and Statistical Modelling

• Perform exploratory data analysis (EDA) and develop statistical models to uncover insights and patterns in large datasets.
• Conduct hypothesis testing, A/B testing, and other statistical analyses to drive data-informed decision-making across the organisation.
• Deliver actionable insights through data storytelling and visualisation tools to communicate findings to non-technical stakeholders.

Data Strategy and Stakeholder Collaboration

• Collaborate closely with data engineers, analysts, and business teams to understand data requirements, ensuring data availability, quality, and relevance for analytical use.
• Engage with key stakeholders to understand business objectives, translating them into data science projects and actionable insights that support company goals.
• Play a key role in defining the data science roadmap, aligning with broader business and technical strategies.

Preferred candidate profile

• Advanced Machine Learning Expertise: Strong proficiency in building and deploying machine learning models, with experience in a variety of ML techniques (e.g., supervised/unsupervised learning, deep learning, time series analysis).
• Programming: Expert-level proficiency in Python and libraries such as Pandas and XGBoost.
• Cloud Proficiency: Experience with AWS ML tools, including SageMaker and Glue.
• Data Visualisation: Proficiency in data visualization tools and libraries to communicate complex insights effectively.