Data Scientist - Growth & Strategic Finance

Wise
London
8 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist (NLP & LLM Specialist)

Data Scientist - Measurement Specialist

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Social network you want to login/join with:

Data Scientist - Growth & Strategic Finance, London

col-narrow-left

Client:

Wise

Location:

London, United Kingdom

Job Category:

Other

-

EU work permit required:

Yes

col-narrow-right

Job Reference:

e9ed601678eb

Job Views:

29

Posted:

22.06.2025

Expiry Date:

06.08.2025

col-wide

Job Description:

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 and .

Job Description

We’re looking for a Data Scientist to join our growing Growth & Strategic Finance 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 and millions of our customers.

We are seeking a skilled and detail-oriented Data Scientist 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:

Data Analysis and Visualization

Collect, clean, and process large financial and operational datasets from multiple sources.

Develop and maintain interactive dashboards, reports, and visualisations to provide clear and actionable insights for FP&A stakeholders.

Leverage statistical methods to analyse trends, measure business performance, and assess financial impacts.

Predictive Modeling & Forecasting

Design and build predictive models and machine learning algorithms to forecast key financial metrics, including revenue, expenses, profitability, and cash flow.

Develop scenario analyses and sensitivity models to support budgeting, forecasting, and long-term financial planning processes.

Work with finance team members to embed models within FP&A processes, improving forecasting accuracy and decision-making capabilities.

Operational Efficiency & Automation

Identify and implement automation opportunities within data collection, reporting, and financial planning processes.

Build data pipelines and improve data infrastructure, ensuring that accurate and timely data is accessible.

Data Quality & Governance

Ensure data integrity and accuracy by implementing robust data validation techniques.

Train and educate team members on best practices for data usage and reporting.

Strategic Insights and Business Impact

Perform ad-hoc analyses to provide actionable insights for senior leadership on specific business questions or strategic initiatives.

Collaborate closely with cross-functional teams to understand business needs, translate them into analytical questions, and deliver insights that drive business performance.

Communicate findings and recommendations in a clear, concise manner to both technical and non-technical audiences.

A bit about you:

Demonstrated experience building and deploying machine learning models in a business environment. Experience with financial modelling, forecasting, and scenario analysis is a plus

Strong Python knowledge and software engineering skills. Ability to read through code. Demonstrable experience collaborating with engineering and analytics;

A strong product mindset with the ability to work independently in a cross-functional and cross-team environment;

Great communication and presentation skills and ability to get the point across to non-technical individuals;

Strong problem solving skills with the ability to help refine problem statements and figure out how to solve them.

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.

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


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.