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ViaBill operates at the crossroads of the eCommerce, payments, and consumer credit sectors and is an early market entrant in the BNPL industry experiencing rapid growth. Being the best at something isn't easy. It requires skill, hard work, and an unprecedented level of ambition and determination. Our team is international, experienced, and most importantly, hardworking. Within a few years, we've grown from our first office in an art gallery in Denmark to having our teams stationed worldwide.
From serving a handful of customers to managing transactions worth more than $150M per year, we're not afraid of new challenges and markets. Our strategy has been validated with external investment from leading VC's Headline and BlackFin Capital Partners.
About the Role:
As a Data Scientist, you will be responsible for solving problems under the umbrella term of 'advanced analytics': data analysis, statistical modeling, machine learning. An example of this includes building credit decision models, validating data source predictive power, or fraud detection models, as well as providing data-backed input for important business decisions.
Furthermore, part of the job is deploying and maintaining existing decision systems, including simple data engineering activities, such as writing SQL scripts that transform raw data into features.
You will participate in building an AWS-backed machine learning platform (data science workbench), within a scope that you are comfortable with. This role requires an understanding of the practical application of machine learning and advanced analytics tools, from raw data transformation, through building machine learning or statistical models, to deployment and maintenance of these models. You will have considerable freedom in selecting tools and methods, significantly influencing the design of the platform upon which you will work.
You need to effectively collaborate with internal stakeholders and cross-functional teams to solve problems and create operational efficiencies.
This position is a full-time, remote basis.
About you:
You have hands-on experience developing machine learning models, which you can demonstrate with previous projects (commercial projects, your portfolio at git repository, or Kaggle competitions). You have experience with machine learning pipelines, data visualization, data validation, statistical testing, and the ability to present findings to non-technical audiences. Relevant experiences in areas like risk management (fraud/credit), consumer lending, consumer finance, and/or business growth are preferred, but we value all areas where you've created classification models or regressions. You understand machine learning techniques such as logistic regression, gradient boosting algorithms, and have a basic understanding of neural network architecture.
You have machine learning programming skills (Python and SQL), with knowledge of ML frameworks (scikit-learn, pandas, NumPy, Keras/tf/pytorch, matplotlib). You understand basic cloud tools (AWS, Google, Azure). You're self-driven with the ability to work in a self-guided manner. You possess strong English skills, as we are an international team. Familiarity with data science frameworks like Kedro is an advantage.
About our team:
We are a team of highly motivated developers who work remotely from our offices. We operate much like open-source projects with core maintainers for our services. Each developer enjoys considerable freedom working in a flat hierarchy in a streamlined process where domain experts are easily accessible on Slack or via Meet. We have a rapid release schedule, often releasing multiple times per day. This provides us with quick and motivating feedback, making it easy for developers to see their impact on the business. It also enables us to experiment and adopt new trends/frameworks quickly.
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