Applied Scientist

Team: Data

Location: Seattle, Washington

Commitment: Full Time

Workplace Type: onsite

Salary:

What you'll do

• Research & Analysis: Perform data research and analysis using Grid's proprietary dataset as well as other relevant sources
• Model Development: Develop and validate models that enable strategically relevant business objectives, such as enabling growth, mitigating fraud, controlling risk, etc.
• Deployment & Iteration: Iterate on new and existing models based on feedback from team and real-world performance
• Productionization: Collaborate with data engineers, product managers to help translate your work into production-grade, high scaled data products
• Present Findings: Present your findings and communicate with members of the team with varying levels of technical depth
• Foster DS @ Grid: Help build out our Applied Science and Machine Learning as a team and practice at Grid

What we're looking for:

• Applied Science Expertise: Proven experience in Machine Learning and/or Applied Science, including a strong background in statistical inference, machine learning. This is a requirement, a bachelors or master's degree in Statistics, Mathematics, Physics, or Computer Science with a focus on machine learning is required. We are currently not accepting applicants with bachelor or master's degrees in Business Analytics, Information Systems, or Data Science.
• Deep Expertise in Applied Science & Machine Learning: 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.
• Research to Implementation Proficiency: A strong track record of reading, understanding, and implementing research papers in machine learning or related fields.
• Robust Technical Skills: Hands-on experience with Python (with libraries like PyTorch/TensorFlow) and SQL is essential.
• Autonomy and Initiative: Ability to work independently and take ownership of projects, showcasing a proactive approach to identifying key leverage points for data products.
• Curiosity and Optimism: People who are constantly asking why the world around them works the way it does, and who have the will to change it.
• Technical Skills: 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).
• Self Starter: Confidence to prioritize work and delivery demonstrable results on a tight cadence.
• Domain Knowledge: Demonstrated experience or understanding of the financial industry, especially in the context of building and scaling FinTech products.