Data Scientist

About Us:

 Sunbit builds financial technology for real life. We use cutting-edge innovations in financial technology to bring leading data and features that allow individuals to be qualified instantly, making purchases at the point of sale fast, fair, and accessible for consumers from all walks of life. We create value focused on our core values; we work tirelessly to ensure that Sunbit becomes available to everyone, everywhere.

Sunbit was included on the 2022 Inc. 5000 list, and has also been named a Most Loved Workplace®, Best Point of Sale Company, and a Top Fintech Startup by CB Insights.

We invite you to #UnleashYourCuriosity and join our ever-growing R&D organization.

Sunbit is looking for an experienced, independent team player who has great data & computer science skills, a passion for data, and excellent analytical and algorithmic skills. 

This role is diverse, encompassing machine learning development and backend engineering. You will play a crucial role in shaping our anti-fraud models and strategies for the company’s core product. Our Data Science team is part of the R&D group so you will work closely with engineers and product managers as well.

Key Responsibilities:

• Research and develop statistical behaviors, study domain-specific data, examine new data sources.
• Develop state-of-the-art machine learning models end to end, including development, deployment, and continuous improvement.
• Operate backend infrastructure for training and deploying ML models, ensuring optimal performance and reliability.
• Develop and maintain Python code for translating ML model outputs into financial decisions.
• Conduct analytical research on our model’s impact on the portfolio, perform ad-hoc analysis for investigating suspicious trends.
• Strategize and implement changes to enhance portfolio performance.

Requirements

• Preferably M.Sc or Outstanding Graduate B.sc in quantitative discipline or equivalent (preferably in Data Science, Computer Science, Mathematics, Statistics, or another related field with a strong emphasis on quantitative analysis)
• Knowledge of Data Science techniques, algorithms, and processes. Prior knowledge in Anomaly detection field is an advantage 
• 2+ years of experience as a data scientist (academic experience included).
• Proficiency in data manipulation and visualization and learning packages (pandas , numpy, polars, scikit-learn, pytorch) 
• Advanced knowledge of SQL.
• Excellent analytical and algorithmic skills. Being able to infer conclusions and creatively offer solutions based on data analysis.
• Excellent communication and teamwork skills.
• Strong ownership and independence skills.