Senior Data Science Manager - Financial Planning and Analysis

Posted 3 Days Ago
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London, Greater London, England
Hybrid
Senior level
Fintech • Mobile • Payments • Software • Financial Services
Wise is one of the fastest growing fintechs in the world and we’re on a mission to make money without borders a new norm
The Role
Seeking a Senior Data Science Manager to lead FP&A, drive analytics, build models, and support strategic decision-making across the company.
Summary Generated by Built In
Company Description

Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.

Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.

As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.

More about our mission and what we offer.

Job Description

Senior Data Science Manager — Financial Planning and Analysis

We’re looking for a Senior Data Science Manager to join our growing Financial Planning and Analysis Team in London. 

This role is a unique opportunity to work behind the scenes of company transactions, understand how we grow and at the same time provide our customers with the seamless service they deserve. What you build will have a direct impact on Wise’s mission and millions of our customers.

About the Role: 

We are seeking a skilled and detail-oriented Data Science Senior Manager  to join our Financial Planning and Analysis (FP&A) team. This role will drive data analytics, build predictive models, and leverage machine learning to support strategic decision-making across the whole company. 

As a member of the FP&A team, you will partner closely with finance, operations, and product teams to uncover insights, forecast trends, and identify areas for operational efficiency and revenue growth. This position offers a unique opportunity to influence business strategy by transforming complex datasets into actionable insights, enabling data-driven decision-making across the organisation.

Here’s how you’ll be contributing:

  • Strategic Technical Leadership: Drive the technical vision for the time series forecasting and causal inference-based models and pipelines. Make key decisions on technology adoption and guide your team through complex technical challenges
  • Team Development & Mentorship: Lead and grow our technical team, mentoring data scientists on cutting-edge technologies and methodologies. Build technical capabilities across the team while fostering career development and knowledge sharing
  • Cross-Functional Leadership: Partner strategically with Product, Engineering, and Operations leaders to ensure that data science is effectively used to enhance product roadmaps. Influence stakeholders to ensure technical solutions deliver maximum customer and business value
  • Delivery Excellence: Establish and oversee scalable deployment strategies and MLOps practices. Lead your team in implementing robust model monitoring, A/B testing frameworks, and performance tracking to ensure production success
  • Technical Strategy: Design comprehensive data strategies and oversee large-scale model optimisation efforts. Ensure your team delivers high-quality model outputs that meet business requirements
  • Organisational Impact: Define and enforce technical standards, model governance frameworks, and best practices across all data science projects. Drive process improvements that accelerate iteration speed and delivery quality
  • Innovation Culture: Shape the research agenda and evaluate emerging AI/ML technologies for strategic adoption. Foster a culture of experimentation and continuous learning while making informed decisions about resource allocation
  • Responsible AI Leadership: Champion ethical AI practices across the organization, establishing frameworks for bias mitigation and transparency while guiding the team in responsible AI development

Additional Information

For everyone, everywhere. We're people building money without borders  — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.

Top Skills

Causal Inference
Data Analytics
Machine Learning
Predictive Modeling
Time Series Forecasting

What the Team is Saying

Lindsay
Surendra
Smrithi
Pavan
Asya
Jennifer
Lauren
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The Company
6,500 Employees
Year Founded: 2011

What We Do

Wise is a global technology company, building the best way to move and manage the world's money.

With Wise Account and Wise Business, people and businesses can hold 40 currencies, move money between countries and spend money abroad. Large companies and banks use Wise technology too; an entirely new network for the world's money. Launched in 2011, Wise is one of the world’s fastest growing, profitable tech companies.

In fiscal year 2025, Wise supported around 15.6 million people and businesses, processing over $185 billion in cross-border transactions and saving customers around $2.6 billion.

Why Work With Us

We’re truly global in who we are, how we work, and how we build. Everything we do is centred around creating a world of money that’s fast, easy, fair. And open to all. Everyone who works here owns a piece of Wise, from the work they do, to the stock they hold.

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Wise Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

We expect new joiners in the office most days to build connections and learn from colleagues for their first six months. After that, most Wisers split their working week between the office and home, typically coming in at least 12 times a month.

Typical time on-site: Not Specified
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