ViaBill operates at the intersection of the eCommerce, payments and consumer credit sectors, and is an industry pioneer and early market entrant in the BNPL industry, which is experiencing rapid growth. Becoming the best at something isn’t easy. It takes skill, hard work, and an unmatched level of ambition and determination. Our team is international, experienced and most of all, hardworking. In just a few years, we’ve grown from our first office in an art gallery in Denmark to having our teams located all over the globe.
From a handful of customers to handling transactions worth more than $150M per year, we aren’t afraid of new challenges and new markets. Our strategy has been validated with external investment from leading VCs Headline and BlackFin Capital Partners.
About The Role
As a Data Scientist / Machine Learning Engineer you will be responsible for solving problems that fall under the umbrella term ‘advanced analytics’: data analysis, statistical modeling, machine learning, ex. you will be responsible for building credit decision models, or fraud detection models, validating data source predictive power, as well as providing data-backed input for business crucial decisions.
Moreover, part of a job is deploying and maintaining existing decision models as well as simple data engineering tasks, ex. writing SQL scripts that transform raw data into features.
You will be taking part in building an AWS-backed machine learning platform (data science workbench) – in the scope you are comfortable with. Overall this role requires an understanding of the practical application of machine learning and advanced analytics tools, starting from raw data transformation, through building machine learning or statistical models, and up to deployment and maintenance of the models. It also gives a lot of freedom in tools and methods selection, and you will have a real impact on the design of the platform you will be working on.
You will need to collaborate effectively with internal stakeholders and cross-functional teams to solve problems and create operational efficiencies.
This is a full-time position on a remote basis.
- You have hands-on experience developing machine learning models and can prove it with previous projects (commercial projects, your portfolio at git repository or Kaggle competitions)
- Experience with machine learning pipelines, data visualization, data validation, statistical testing, and presenting your findings to non-technical audiences
- Relevant experiences in risk management (fraud/ credit), consumer lending, consumer finance, and/or business growth are preferred but we appreciate all the areas where you’ve created a classification of regressions models
- Understanding of machine learning techniques, such as logistic regression, and gradient boosting algorithms, a basic understanding of neural network architecture
- Machine learning programming skills (Python, and SQL), knowledge of ML frameworks (scikit-learn, pandas, NumPy, Keras/tf/pytorch, matplotlib)
- Basic cloud tools understanding (AWS, Google, Azure)
- Self-driven with the ability to work in a self-guided manner English is a must since we are an international team
- Familiarity with data science frameworks like Kedro is a big plus