At a Glance
- Tasks: Own and operate production infrastructure for FX pricing and risk systems.
- Company: Wise, a global tech company revolutionising money management.
- Benefits: Competitive salary, equity, health coverage, and flexible work options.
- Other info: Diverse and inclusive culture welcoming all passionate learners.
- Why this job: Join a dynamic team and make a real impact in financial technology.
- Qualifications: 4+ years in Python systems with a quantitative background.
The predicted salary is between 60000 - 80000 £ per year.
Wise is a global technology company building the best way to move and manage the world’s money. Their mission is to make international payments easier, faster and cheaper for individuals and businesses worldwide.
We are seeking a talented quantitative developer to join our Treasury Markets Data Science team. This role focuses on owning and operating the production infrastructure behind our FX pricing, risk, and trading systems with the opportunity to broaden the scope of work into traditional quantitative research.
About The Role
You will join the Treasury Markets Data Science team and own the quantitative infrastructure that powers how Wise manages FX risk across over USD 250 bn+ in annual FX volume. You will work with quants, traders, analysts, product managers and engineers to price, hedge, manage and scale FX operations in real time. The team runs a Python‑first, production‑grade quant platform: real‑time curve construction, multi‑instrument pricing, risk analytics and trading strategy. Your primary focus will be keeping these systems reliable, performant and well‑engineered, while contributing to the quantitative models themselves as you grow into the domain.
What You’ll Own
- Python microservices running quantitative models in production
- Monitoring, alerting and reliability for real‑time pricing and risk systems
- Shared quant libraries used across multiple services
- CI/CD pipelines, deployment and operational excellence
- Incident response and root‑cause analysis for production issues
Where you’ll grow
- Real‑time curve construction (yield curves, FX forwards, vol surfaces)
- Pricing models for new instruments and products
- Trading strategy development and optimisation
- Risk modelling alongside the Risk team (VaR, stress testing, scenario analysis)
- Back‑testing frameworks and model validation
- Customer behaviour modelling, pricing strategy and product launch support
- Collaboration with product teams to translate quantitative insights into customer‑facing decisions
Qualifications
- 4+ years building and maintaining production Python systems
- Strong experience with microservices, databases, and production infrastructure
- Experience with streaming systems, real‑time data pipelines or event‑driven architectures (Kafka, Flink, Redis, etc.)
- Quantitative background – maths, physics, engineering or finance – with the ability to read and reason about models
- Experience with testing, monitoring and debugging complex systems under load
- A product mindset focused on user needs
- Clear communication and cross‑functional collaboration skills
It’s a bonus if you are familiar with
- FX or financial markets experience
- Term‑structure modelling, stochastic calculus or Monte Carlo methods
- Interest rate curve bootstrapping
- Algorithmic execution experience
- Data lake or warehouse technologies (Snowflake, Iceberg, Spark, etc.)
We value diversity and inclusion. We welcome applicants from all backgrounds who are passionate about learning and contributing to our mission. We believe diverse teams build better products. If you’re from an under‑represented demographic and thrive in an inclusive environment, we especially encourage you to apply.
Benefits
In addition to a competitive salary, Wise offers equity, comprehensive health coverage, a flexible working environment and opportunities for professional growth.
Lead Data Scientist - Treasury Markets Quant in London employer: hackajob
At hackajob, we pride ourselves on being an exceptional employer that fosters a culture of innovation and inclusivity. Our diverse team thrives in a high-performance environment where your contributions directly impact our multi-asset platform's success. With ample opportunities for professional growth and a commitment to employee development, joining us means being part of a forward-thinking company that values your expertise and ambition.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist - Treasury Markets Quant in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like hackajob!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Lead Data Scientist - Treasury Markets Quant at hackajob.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like hackajob.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Data Scientist - Treasury Markets Quant at hackajob, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead Data Scientist - Treasury Markets Quant 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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at hackajob, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at hackajob. 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 hackajob
✨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!
✨Showcase Your Projects
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
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at hackajob!
✨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.
