Fractional Data Science Lead (FP&A) (2-3 days per week)

G MASS Consulting; Greater London; Hybrid; GBP 239,000 - 314,000; Full time

Benefits offered by this job

Hybrid work in London

Competitive day rate

Job summary

ace is seeking a Data Science Lead to guide a major data programme in a London-based, hybrid setup. The role blends senior advisory responsibilities with hands-on modelling and governance across FP&A and data engineering teams.

The successful candidate will defend modelling decisions to CTOs and teach fundamental data concepts, shaping a scalable data culture within a large financial services client.

Qualifications

• 10+ years in financial services with advisory or lead roles.
• Deep expertise in dimensional modelling and semantic layer architecture.
• SQL expert; Python for analysis and validation.
• Experience with Microsoft data stack: Fabric, Synapse, Power BI, and Azure AI Foundry.

Responsibilities

• Define data mart modelling approach and semantic layer.
• Establish data science fundamentals and KPI framework across teams.
• Review output with structured feedback and ensure reproducibility.
• Deliver education and upskilling to sustain capability.
• Advise on governance implications of downstream AI tooling and data segregation.
• Assess analytical readiness and sequence remediation and cleansing work.

Skills

SQL

Python

Dimensional modelling

Data governance

Executive presence

Tools

Fabric

Synapse

Power BI

Azure AI Foundry

Job description

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.
• Senior practitioner background, typically 10+ years, with meaningful time in an advisory, lead or principal capacity where the deliverable was a recommendation rather than a model.
• Deep expertise in dimensional data modelling, schema design and semantic layer architecture; able to articulate and defend the trade-offs between competing modelling approaches.
• Expert SQL; strong Python for analysis and validation.
• Practical experience of the Microsoft data stack — Fabric, Synapse, Power BI semantic models, and ideally Azure AI Foundry.
• Demonstrable experience defining data quality frameworks and KPIs in an enterprise setting.
• Evidenced capability-building experience. A material part of this role is raising the standard of an existing team.
• Executive presence and credibility with technology leadership and finance stakeholders.
• Comfortable operating at enterprise, not internet, data scale.

Benefits

Please note that this role is expected to be 2 or 3 days per week, hybrid in London. Start date is ideally immediate, with the ability to earn up to £1500 a day.