At Snap Finance, the business problem is to create flexible financing solutions that help people move forward, regardless of credit history, by using data, machine learning, and a more human approach to improve predictions, reduce risk, and empower consumers in the growing alternative finance market.
Requirements
- Proficiency with Python, Java, or other general-purpose programming languages.
- Familiarity with deep learning and traditional classification methods (e.g., Deep Neural Networks, Decision Trees, Random Forest).
- Proficiency and working knowledge of at least one major deep learning framework (e.g. PyTorch, Tensorflow)
- Sequence modeling (e.g.RNNs, Natural Language Processing techniques, Attention-Based Autoregressive models)
- Understanding of basic statistical analysis (e.g., Hypothesis testing, experimental design).
- Exposure to cloud services such as AWS, especially EC2 and S3.
- Basic SQL skills and experience with big data tools and frameworks like Hadoop, Spark, or CockroachDB skills
Responsibilities
- Assist in the development and deployment of scalable models and tools using machine learning and optimization techniques, with guidance from senior team members.
- Collaborate with the data engineering team to gather and integrate data, creating valuable features.
- Participate in assembling large, complex data sets that meet business requirements.
- Contribute to the analysis of customer behavior and optimization of credit risk models.
Other
- MS or PhD in a quantitative field such as Statistics, Econometrics, Mathematics, Physics, Computer Science, or related quantitative discipline.
- BS in the fields described above will be considered if skill set and experience are robust
- 6+ years of experience in one or more of the following areas: machine learning, artificial intelligence, data mining, or related research.
- Willingness to learn and develop skills in automated workflows (e.g., Airflow, Jenkins) and distributed systems.
- Generous paid time off, Competitive medical, dental & vision coverage
