At a Glance
- Tasks: Design and deploy machine learning models to tackle financial crime and risk challenges.
- Company: Join a fast-growing fintech company shaping the future of financial services.
- Benefits: Competitive salary, stock options, performance bonuses, and cutting-edge tools.
- Other info: Collaborative environment with opportunities for growth and innovation.
- Why this job: Make a real impact on fraud detection and help millions of customers.
- Qualifications: 5+ years in Data Science, strong Python skills, and experience with ML models.
The predicted salary is between 70000 - 90000 £ per year.
We're looking for a Senior Data Scientist to help build the intelligence layer behind ARQ's financial products. You'll work on some of our most important risk and financial crime challenges, developing machine learning models that help us detect fraud, prevent losses, and make better decisions at scale. This is a highly impactful role where you'll take problems from idea to production — partnering with Product, Engineering, and Operations teams to turn large-scale datasets into models that directly influence customer and business outcomes. You'll join a fast-growing team helping build the future of financial services for more than 2 million customers across the Americas.
What You'll Be Doing
- Design, build, and deploy machine learning models that solve real-world financial crime and risk challenges.
- Work on problems such as fraud detection, chargeback prediction, anomaly detection, identity verification, and transaction monitoring.
- Transform ambiguous business problems into measurable ML solutions.
- Partner closely with Product and Operations teams to define requirements, success metrics, and decision frameworks.
- Analyse large datasets to identify patterns, opportunities, and risks.
- Build and maintain production-ready data pipelines and model-serving solutions.
- Monitor model performance and continuously improve accuracy, reliability, and business impact.
- Collaborate with Data Engineering and Backend Engineering teams to operationalise machine learning at scale.
- Help shape the future of AI and machine learning capabilities across ARQ.
What You'll Need
- 5+ years of experience across Data Science, Machine Learning, Software Engineering, or related disciplines.
- Strong Python skills and experience working with large-scale datasets.
- Proven experience developing and deploying machine learning models in production environments.
- Strong understanding of supervised learning techniques, particularly classification problems.
- Experience with anomaly detection, fraud prevention, risk modeling, or related domains.
- Ability to translate business challenges into data-driven solutions.
- Experience working cross-functionally with Product, Engineering, and business stakeholders.
- Strong communication skills and a pragmatic approach to problem‑solving.
- Comfortable operating in fast‑moving environments with high ownership.
Nice To Have
- Experience in financial crime, fraud prevention, AML, risk, or payments.
- Experience in fintech, banking, or financial services.
- Backend engineering experience and familiarity with production systems.
- Experience building real-time decisioning or risk platforms.
- Familiarity with modern MLOps practices and model monitoring.
Benefits
- Competitive salary and benefits
- Stock options, so you own part of what you build
- Discretionary performance bonus
- The latest tools and technology
- A world-class team that will challenge and grow your skills
- The opportunity to help build the best fintech app in Latin America
- Office Policy: 3-4 days a week in‑office
Senior Data Scientist employer: ARQ
ARQ is an exceptional employer for a Senior Data Scientist, offering a dynamic work culture that fosters innovation and collaboration. With competitive salaries, stock options, and a discretionary performance bonus, employees are rewarded for their contributions while having access to cutting-edge tools and technology. The opportunity to work alongside a world-class team in a fast-growing fintech environment not only enhances professional growth but also allows you to make a significant impact on financial services for millions of customers across the Americas.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist
✨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 ARQ!
✨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 Senior Data Scientist at ARQ.
✨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 ARQ.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Scientist at ARQ, 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 Senior Data Scientist
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 ARQ, 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 ARQ. 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 ARQ
✨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 ARQ!
✨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.
