Description

KEY RESPONSIBILITIES

• Modelling and analytics: Build, validate, and maintain predictive and statistical models using Python to support product and engineering decisions across a large-scale multi-product environment.
• Exploratory data analysis: Perform EDA on product and operational datasets — identify patterns, anomalies, and opportunities — and communicate findings clearly.
• Experimentation: Design and run controlled experiments (A/B tests, hypothesis testing) and report results with appropriate statistical rigour.
• Dashboarding and reporting support: Develop analytics outputs and reporting layers in support of dashboarding work; collaborate with the Data Engineer on data availability and pipeline requirements.
• Feature engineering: Work with raw and transformed data to build features for models; document all transformations and assumptions.
• Ad hoc analytics: Respond to ad hoc data science and analytics requests raised by the Project Lead within agreed hour estimates.
• Model documentation and reproducibility: Maintain notebooks, model cards, and documentation to a standard that supports knowledge transfer and continuity.
• Collaboration: Partner closely with the Data Engineer (pipelines and data quality) and Cloud Engineer (Azure infrastructure); coordinate with client stakeholders via the Project Lead only.

REQUIRED TECHNICAL SKILL-SET

PREFERRED SKILLS / NICE TO HAVE

• Experience with deep learning frameworks (PyTorch, TensorFlow) for signal or time-series data.
• Exposure to MLflow or equivalent for experiment tracking and model registry.
• Familiarity with Azure Machine Learning Studio or Azure Databricks for model deployment

Key Skills

Education

Any Gradute