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. - 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. - 25
days paid time off per year + public holidays - £500 annual
allowance to spend on anything that will contribute to your mental
or physical health - £500 allowance towards a phone device every 24
months (from your 6th month anniversary) - £500 annual allowance
for learning and training - Cycle to work scheme - Enjoy a flexible
work environment, balancing onsite and working from home
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