At Two, we’re fixing b-commerce with BNPL for B2B! We’re passionate about building solutions that enable B2B Merchants to sell more, faster, and more efficiently. Growing at a rapid pace, our goal is to become the world’s largest B2B payment solution by 2027. Two is built by a passionate and energetic team, all working together to revolutionise the way businesses transact online. We believe in diversity in opinions and ideas and expect all employees to think like an owner.Learn more about Two here.
Location
We are office led, remote-friendly company with talent hubs in London, Oslo, Glasgow, and Stockholm. While our Data Science team is primarily based in London, we’re open to other office locations for the right candidate.
About The Role
We have a data-driven culture. We recognise that in this digital era, data wealth and the ability to leverage it effectively is what will set us apart. As such, we are looking for a high-caliber data scientist to join our wonderful team of talented and experienced data scientists. Ideal candidate should have a passion for building highly scalable and optimised decision engines, as well as providing insights that can drive strong business value.
The role would involve building and deploying Two’s best-in-class decision processes to accurately measure credit risk as well as delivering a great service to our partners. Whatever background you come to us from, you will quickly find you are a world expert in credit management in the B2B e-commerce space.
Technologies We Use
- Python for data exploration, analysis, model design, and deployment
- BigQuery, Postgres, Hadoop, and Spark for distributed data storage and parallel computing
- Hosted on Google Cloud Platform
Your day-to-day will involve
- Carrying out exploratory data analysis on internal and external data sources with the purpose of deriving predictive insights or supporting data-driven decision-making.
- Designing and deploying Machine Learning models onto our decision engine to establish and enhance robust risk management controls, in line with business risk appetite.
- Engaging in feature engineering to identify predictive and effective model inputs.
- Working with data engineers to implement and improve data collection techniques to enhance the data wealth of the business for insights and model design.
- Developing processes and tools to monitor decision processes outcomes to identify areas of optimisation.
Requirements
- 1-3 years of experience manipulating data sets and building statistical and machine learning models
- University degree in fields such as Data Science, Mathematics, Physics, Computer Science, Software Engineering or any other quantitative field or strong relevant experience.
- Proficiency in using Python and SQL to query databases, manipulate data and draw insights from large data sets.
- Strong familiarity with key Python packages for Data Science such as Pandas, SciKit-Learn, Statsmodels, NumPy, SciPy, Matplotlib, TensorFlow, Keras, etc.
- Good knowledge of key machine learning techniques such as clustering, tree methods, boosting, text mining, artificial neural networks, etc.
- Good knowledge of advanced statistical techniques and concepts such as regression techniques, simulation, resampling methods, stratification and sample selection, hypothesis testing, etc.
- Self-starters with strong business acumen, an entrepreneurial mindset, and problem-solving skills with an emphasis on product development.
- Excellent written and verbal communication skills for communicating effectively in teams of technical and non-technical individuals.
- Adept to working in a rapidly changing environment with dynamic objectives and iteration with users.
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
