Senior Data Science Manager - Finance

Reposted 9 Days Ago
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London, Greater London, England, GBR
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
Lead a team of data scientists to build and productionize time series forecasting, causal inference, and ML models for FP&A. Drive MLOps, model monitoring, A/B testing, cross-functional partnership with finance/product/engineering, and responsible AI governance to inform strategic planning and operational efficiency.
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

THE ROLE

We are looking for an experienced detail-oriented Senior Data Science 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 Wise.

With your team of (5) Data Scientists, 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 and enabling data-driven decision-making. What you and your team build will have a direct impact on Wise’s mission and millions of our customers worldwide.

Here’s how you’ll be contributing:

  • Technical Leadership & Innovation: Drive the technical vision for the time series forecasting and causal inference-based models and pipelines. Making key decisions on technology adoption and guiding your team through complex technical challenges. You will also shape the research agenda and evaluate emerging AI/ML technologies for strategic adoption, fostering a culture of experimentation and continuous learning

  • Team Development & Mentorship:  Lead, mentor, and grow our talented data scientists, building technical capabilities across the team, fostering career development, and promoting knowledge sharing on cutting-edge technologies and methodologies.

  • Cross-Functional Collaboration & Customer Impact: Partner strategically with Product, Engineering, and Operations leaders to embed data science effectively into product roadmaps. Strengthening a culture that prioritises tangible customer solutions and measurable impact over isolated experiments.

  • Delivery Excellence & Operationalisation: Establish and oversee scalable deployment strategies, robust MLOps practices, model monitoring, A/B testing, and performance tracking to ensure production success. Drive process improvements that accelerate iteration speed and delivery quality across all data science projects.

  • 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

  • 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

Qualifications

WHAT YOU’LL BRING

  • Proven Leadership & Team Development: Experience leading high-performing data science teams, driving the development of production-grade Machine Learning and AI systems at scale, and delivering measurable business outcomes. Demonstrated ability to build, scale, mentor, hire, and grow both individual data scientists and data science leaders. 

  • Deep Technical & AI/ML Expertise: Strong technical foundation with expertise in coding (Python, SQL), advanced modeling (Tree-based, Neural Networks, Deep Learning), GenAI frameworks (LlamaIndex, LangGraph, etc.), and cloud platforms (AWS, GCP, Azure). Proven ability to architect scalable solutions, design comprehensive data strategies, and guide technical decision-making for complex AI/ML challenges and MLOps practices.

  • Domain Expertise: Experience in Financial Modeling & Forecasting. You have a deep understanding of financial planning processes, budgeting, variance analysis, and building predictive models for revenue, costs, and business performance metrics. Proficiency in time series analysis, econometric modeling, scenario planning, and statistical techniques for financial data analysis, including experience with financial modeling platforms to build robust predictive and prescriptive analytics solutions.

  • Strategic Communication & Influence: The ability to influence senior leadership, translate complex technical concepts into strategic business language, build consensus across diverse stakeholder groups, and champion responsible AI practices.

  • Ownership & Pragmatism: Demonstrated ability to proactively identify impactful opportunities, influence business strategy, and drive initiatives to completion. You possess a pragmatic approach, effectively triaging requests and adapting analysis scope to achieve optimal outcomes in a fast-paced environment.

NICE TO HAVE BUT NOT ESSENTIAL

  • Experience in financial services, fintech, or other regulated industries is highly valued, with leadership experience navigating compliance requirements, risk management, and regulatory frameworks

  • An advanced degree (Masters/PhD) in Computer Science, Data Science, Machine Learning, or related quantitative field preferred

Additional Information

#LI-CH1

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

A/B Testing
AWS
Azure
Causal Inference
Deep Learning
Econometric Modeling
GCP
Langgraph
Llamaindex
Mlops
Model Monitoring
Neural Networks
Python
SQL
Time Series Forecasting
Tree-Based Models

What the Team is Saying

Surendra
Smrithi
Pavan
Jennifer
Lindsay
Lauren
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The Company
8,000 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
Austin, TX
Brussels, BE
Hungary
Hyderabad, IN
Kuala Lumpur, MY
London, GB
New York, NY
São Paulo, BR
Singapore
Tallinn, EE
Tokyo, JP
Learn more

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