Data Science Lead

ace has partnered with a prestigious, leading international fund and corporate services organisation to appoint an experienced Data Science Lead who will provide senior advisory and technical guidance across a foundational data programme, at the point where the structural decisions are being made. 

Our client is midway through a significant change agenda: consolidating a fragmented core administration estate and investing seriously in clean data, consistent data processes, and data quality measurement. This is a senior role focused on judgement and output. You will sit alongside an internal data lead, a small data engineering function and an FP&A team, and your mandate is to make sure they build the right thing — bringing genuine data science fundamentals to the team, challenging structural decisions before they become expensive, and leaving the organisation more capable than you found it.  

The successful candidate will be equally comfortable defending a modelling decision to a CTO and teaching a schema fundamental to an FP&A analyst. 

Responsibilities: 

• Define and govern the modelling approach for the client's new data mart: schema selection, grain, conformed dimensions and semantic layer structure — with explicit focus on avoiding decisions that foreclose future analytical or AI use cases. 

• Establish data science fundamentals and good practice across the FP&A and data engineering teams: 

• Modelling discipline, statistical rigour, reproducibility and documentation standards 

• Review of the team's own output, with structured feedback 

• Define data quality dimensions, KPIs and measurement approach, and advise on how these are instrumented and reported. 

• Assess the analytical readiness of the current data estate and set the sequencing for remediation and cleansing work. 

• Deliver structured education and upskilling — working sessions and written standards — so capability persists beyond the engagement. 

• Advise on the data governance implications of downstream AI and agentic tooling, including client-data segregation, permissible-use controls and auditability. 

• Provide a clear, honest read on where the client genuinely needs sustained data science capability versus where existing capability simply needs tuning. 

Requirements

• Substantial financial services domain experience — fund administration, corporate or fiduciary services, asset servicing, wealth, banking or insurance. Candidates must understand the fundamentals of the business, not only the shape of the data.