About Us
At Two, we are redefining B2B payments by creating innovative, data-driven solutions that power seamless commerce for businesses worldwide. Our Risk Engine is at the core of this mission, utilising cutting-edge machine learning and AI to drive fraud prevention, credit risk management, and business scalability. We are seeking a visionary VP of Data Science to lead our team and help shape the future of our Risk Engine and data science initiatives.
About the Role
As the VP of Data Science, you will spearhead Two’s data science strategy, innovation, and execution. Reporting directly to the CEO, you will lead and mentor a team of 7-10 data scientists, collaborating across functions to deliver state-of-the-art solutions. Your leadership will ensure our Risk Engine evolves as a product, stays ahead of market trends, and delivers measurable results.
This role combines strategic vision with hands-on expertise, driving advanced statistical modeling, machine learning solutions, and actionable insights to enhance growth, manage risk, and deliver outstanding business impact.
Location
We are office led, remote-friendly company with talent hubs in Oslo, London, Glasgow, and Stockholm. Our ideal candidate should be based in one of these office locations.
Key Responsibilities
- Collaborate with business strategy teams to identify and prioritise high-impact decision science challenges, ensuring alignment with organisational objectives.
- Define and execute a visionary Risk Product Strategy, supported by a comprehensive five-year roadmap aligned with business growth goals.
- Spearhead the development of cutting-edge credit risk and fraud prevention models powered by machine learning and AI models, driving both scalability and operational excellence.
- Deliver actionable insights and strategic recommendations to C-level executives, stakeholders, and investors to inform key business decisions.
- Explore and leverage unconventional data sources to create innovative, data-driven solutions for complex challenges.
- Combine traditional statistical methods with state-of-the-art machine learning techniques to address issues in credit risk, fraud prevention, pricing, and customer retention.
- Establish and enforce best practices for model development, validation, testing, and documentation, ensuring consistency and reliability.
- Mentor and inspire a high-performing team, fostering a culture of innovation, collaboration, and open communication.
Who You Are:
- A seasoned data science leader with a strong strategic vision and a proven track record of delivering innovative, scalable solutions.
- Experienced in leading diverse teams across data science, engineering, and analytics, with a focus on mentoring and professional development.
- Adept at aligning data science initiatives with broader business objectives and driving measurable results.
- Passionate about staying ahead of industry trends and fostering a culture of innovation and continuous learning.
Requirements
- 8+ years’ experience in data science, modelling, or analytics.
- Proven expertise in Python/SQL coding and credit risk modelling.
- Advanced knowledge of machine learning techniques (e.g., deep learning, random forests, clustering, anomaly detection).
- Strong understanding of Credit bureau data.
- Hands-on experience in data pipelines, ML Ops, and building scalable solutions.
- Excellent communication skills to present technical insights to diverse audiences.
Desirable:
- Experience in financial services, particularly consumer credit.
- Background in fraud modelling and data services.
- Scale-up experience in a fast-growing business.
Benefits
- 25 days paid time off per year + public holidays
- £500 annual allowance to spend on anything that will contribute to yourmental or physical health
- £500 allowance towards aphone deviceevery 24 months (from your 6th month anniversary)
- £500 annual allowance forlearning and training
- Equity – get options at Two as part of your offer, and have a real stake in the company’s success
- Find your best way to work with our office-led, remote-friendly working framework!
