At a Glance
- Tasks: Lead data science initiatives and provide expert guidance on data modelling and quality.
- Company: Join a prestigious international fund and corporate services organisation.
- Benefits: Earn up to £800 a day with hybrid working in London.
- Other info: Opportunity to work in a dynamic environment focused on innovation and growth.
- Why this job: Make a real impact by shaping data strategies and enhancing team capabilities.
- Qualifications: 10+ years in financial services with strong data modelling and SQL skills.
The predicted salary is between 56250 - 68750 £ per year.
Description ace has partnered with a prestigious, leading international fund and corporate services organisation to appoint an experienced Senior Data Scientist 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 full-time, five days per week, hybrid in London. Start date is ideally immediate, with the ability to earn up to £800 a day.
Senior Data Scientist (FP&A) (Full-time) in London employer: G MASS
G MASS is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets finance. Employees benefit from a collaborative culture that encourages professional growth and development, alongside competitive remuneration and flexible working arrangements. With a focus on enhancing broker data handling capabilities, this role provides a unique opportunity to work with cutting-edge technology and contribute to impactful projects within a leading hedge fund.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist (FP&A) (Full-time) in London
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We think you need these skills to ace Senior Data Scientist (FP&A) (Full-time) in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at G MASS. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at G MASS
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.