BNPL with a Modern Tech Stack is Looking for a Remote Senior Data Scientist

In order to reach our growth expectations and optimize our business process, we’re now looking for a SeniorData Scientist to join the exciting journey.

As a Senior Data Scientist, you will be responsible for modeling complex problems, discovering insights and identifying opportunities through the use of statistical, machine learning, algorithmic and experimental techniques. You will need to collaborate effectively with internal stakeholders and cross-functional teams to solve problems and create operational efficiencies. The candidate should be able to apply a breadth of tools, data sources and analytical techniques to answer a wide range of business questions and present the insights. Additionally, the candidate should be highly motivated to drive issues to resolution.

You are playing a pivotal role to detect fraud and mitigate loss and generate insights about marketing, our merchants, and customers. You will help build rich datasets and state-of-art machine learning techniques to develop data products that are used by thousands of users and contribute to the growth of our business.

This is a full-time position on remote basis.

About you:

• You have 3-4 years of hands-on experience developing machine learning models at scale from inception to business impact
• Relevant experiences in risk management (fraud/ credit), consumer lending, consumer finance, and/or business growth are preferred
• Quantitative background in computer science, statistics, math, and similar majors
• Deep understanding of modern machine learning techniques, such as logistic regression, random forest, classification, and recommendation systems
• Strong machine learning programming skill (Python, R, and SQL preferred)
• Willingness to learn new technologies/languages
• Self-driven with the ability to work in a self-guided manner
• English is a must since we are an international team

What you will do:

• Evaluate potential approaches, build features, algorithms, and determine metrics to improve the current machine learning models
• Develop machine learning systems - from data pipelines and training to real-time prediction engines
• Work cross-functionally with Product owners, engineers, DevOps and other business units to improve our existing machine-learning systems
• Identify new opportunities to apply Machine Learning to different parts of the product
• Stakeholder management (both internally, and externally - for example, vendors) is a fairly big part of the role