Senior Fractional Data Science Lead — Enterprise Data Strategy

G MASS; Greater London; Hybrid; GBP 221,000 - 332,000; Full time

Benefits offered by this job

Hybrid work in London

Job summary

G MASS is partnering with an 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 in London. The role is hybrid (2–3 days/week) with an immediate start and a daily rate up to £1500.

You will establish data governance, data quality KPIs, and education for FP&A and data engineering teams, defending modelling decisions to CTOs and raising capability across the

Qualifications

• 10+ years in a senior advisory/lead capacity within financial services
• Deep expertise in dimensional data modelling and schema design
• Expert SQL and Python for analysis and validation
• Experience with Microsoft data stack (Fabric, Synapse, Power BI) and Azure AIFoundry
• Ability to defend modelling decisions to technology leadership

Responsibilities

• Define and govern the modelling approach for the data mart with emphasis on future AI use cases
• Establish data science standards and best practices across FP&A and data engineering teams
• Review outputs and provide structured feedback
• Define data quality KPIs and instrumentation
• Assess analytical readiness and plan remediation sequencing
• Deliver education sessions and written standards for capability uplift
• Advise on data governance implications of downstream AI and tooling

Skills

SQL

Python

Dimensional modelling

Schema design

Semantic layer

Executive presence

Tools

Fabric

Synapse

Power BI

Azure AI Foundry

Job description

G MASS is partnering with an 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 in London. The role is hybrid (2–3 days/week) with an immediate start and a daily rate up to £1500.

You will establish data governance, data quality KPIs, and education for FP&A and data engineering teams, defending modelling decisions to CTOs and raising capability across the