Responsibilities
- Perform data research and analysis using Grid's proprietary dataset as well as other relevant sources
- Develop and validate models that enable strategically relevant business objectives, such as enabling growth, mitigating fraud, controlling risk, etc.
- Iterate on new and existing models based on feedback from team and real-world performance
- Collaborate with data engineers, product managers to help translate your work into production-grade, high scaled data products
- Present your findings and communicate with members of the team with varying levels of technical depth
- Help build out our Applied Science and Machine Learning as a team and practice at Grid
Requirements
- Proven experience in Machine Learning and/or Applied Science, including a strong background in statistical inference, machine learning
- A bachelor's or master's degree in Statistics, Mathematics, Physics, or Computer Science with a focus on machine learning is required
- Proven experience in applied machine learning, including a deep understanding of statistical inference and predictive modeling
- Demonstrated practical experience with deep learning techniques, particularly transformer-based models
- A strong track record of reading, understanding, and implementing research papers in machine learning or related fields
- Hands-on experience with Python (with libraries like PyTorch/TensorFlow) and SQL is essential
- Ability to work independently and take ownership of projects, showcasing a proactive approach to identifying key leverage points for data products
- People who are constantly asking why the world around them works the way it does, and who have the will to change it
- Proficiency in the modern machine learning techniques, such as Model Evaluation and Validation, Deep Learning and Time Series Analysis, Logistic Regression, Naive Bayes, Tree based Models (i.e., Random Forest)
- Confidence to prioritize work and delivery demonstrable results on a tight cadence
- Demonstrated experience or understanding of the financial industry, especially in the context of building and scaling FinTech products
Work Arrangement
On-site — Seattle
