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Are you passionate about data-driven innovation, building best-in-class data products, and delivering impactful business insights? Do you have strong technical expertise in Python, SQL, and experience in analysing and modeling data? Are you eager to work in a fast-paced, cross-functional team within an early-stage startup, where you can take ownership and actively shape our data strategy? If so, we would love to hear from you!
At Two, we are revolutionising B2B payments by bringing the best of B2C e-commerce to the B2B world. Our innovative, data-driven solutions empower businesses to sell more, faster, and more efficiently, creating a seamless commerce experience. With an impressive 30% month-on-month growth rate, our ambition is to become the world’s largest B2B payment solution by 2027.
Backed by leading VCs such as Sequoia, Shine, LocalGlobe, Antler, and Posten, along with influential Fintech angel investors, we’ve raised over €30 million to date. Now, we’re expanding our team to continue reshaping the future of B2B payments.
About the role:
We are looking for a Mid or Senior-Level Data Scientist to join our high-performing team, united by a passion for data excellence. This is an exciting opportunity to work in a dynamic, fast-paced environment, where data science plays a crucial role in risk management, fraud detection, customer behavior analytics, and automation of financial processes.
In this role, you will apply machine learning, advanced statistical techniques, and large-scale data processing to develop models that enhance our BNPL platform. You will work closely with Engineering, Risk, and Product teams to deploy scalable, data-driven solutions that fuel business growth.
Key Responsibilities:
- Develop and deploy machine learning models to optimise credit risk assessment, fraud detection, and transaction automation.
- Analyse large datasets to extract meaningful insights and drive data-informed decision-making.
- Enhance our data pipelines and machine learning infrastructure, ensuring efficient model training and deployment.
- Collaborate with engineering, product, and risk teams to integrate data science solutions into real-time production environments.
- Conduct statistical analyses and A/B testing to validate hypotheses and improve model performance.
- Continuously research and experiment with emerging techniques in machine learning, deep learning, and data analytics.
Requirements
- 3-5 years of experience in data science, machine learning, or a related field.
- Strong programming skills in Python and SQL, with the ability to query databases and manipulate large datasets.
- Proficiency in key Python libraries for data science, including Pandas, Scikit-learn, Statsmodels, NumPy, SciPy, Matplotlib, TensorFlow, and Keras.
- Solid understanding of machine learning techniques, such as clustering, tree-based methods, boosting, text mining, and neural networks.
- Expertise in statistical modeling and techniques such as regression, hypothesis testing, simulation, resampling methods, and stratification.
- Degree in Data Science, Mathematics, Physics, Computer Science, Engineering, or another quantitative field (or equivalent experience).
- Strong business acumen with a problem-solving mindset, ideally with experience in fintech or payments.
- Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
- Ability to work in a dynamic, fast-paced environment, adapting to changing priorities and objectives.
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