Senior Analyst Data Pilots, Data Exploration and Partnerships

About the role

This contract role sits within the Data Exploration and Partnerships (DXP) department and acts as the technical counterpart to the Data Partnership Managers. While the Partnership Manager defines the business problem and secures the data, you will dig into the data, test hypotheses and build proof-of-concept dashboards and analyses. The aim is to turn raw, messy data from pilots and partnerships into prototypes that show what is possible, and provide the evidence that decides which pilots become scaled products.

Key responsibilities

• Work with Data Partnership Managers in later-stage discussions to finalise the scope and specifications of data exchanges, and assess the quality, structure and feasibility of using STB's and partner data.
• Translate business problems into specific, testable analytical questions.
• Lead the technical pilot process: data ingestion, cleaning and transformation of new datasets, and advanced exploratory data analysis (EDA) with Python.
• Apply statistical methods to test hypotheses and build simple predictive models where applicable.
• Design and build interactive proof-of-concept dashboards (for example with AWS QuickSight) that answer business questions clearly for non-technical stakeholders.
• Turn complex findings into clear insights and recommendations, and present pilot results to internal and external stakeholders.
• Document data sources, cleaning logic, methodologies and code, and create a "recipe book" for each successful pilot so it can be handed over to the Data & AI Product Manager for evaluation and potential productisation on the STAN platform.

Required profile

• Trained in a quantitative field such as Data Analytics, Computer Science, Statistics, Engineering or Economics.
• At least 3 years of hands-on experience in exploratory data analysis, data ingestion, cleaning, aggregation and data modelling with large, real-world datasets.
• Strong applied statistical knowledge (regression, classification, significance testing).
• A portfolio of projects (GitHub, personal website or slide deck) showing hands-on data analysis and coding.
• Team player who owns their technical workstream end-to-end, comfortable with ambiguity and able to turn vague problems and messy data into a path forward.

Required skills

• Statistical programming (Python) and SQL are essential.
• Data visualisation and dashboarding; AWS QuickSight is highly advantageous.