Applied Scientist

Grid•Seattle, WA

4d•$120,000 - $140,000•Onsite

About The Position

Grid is seeking an Applied Scientist to join their team and contribute to the development and scaling of core product lines. This role involves close collaboration with product, engineering, and business leaders to leverage data for impactful decisions. The position offers access to robust datasets and clear research objectives, providing an opportunity to significantly influence the company's progress and user satisfaction. Projects will focus on areas such as fraud detection, prevention, and mitigation in new domains, risk underwriting for lending programs, and predictive analytics for financial systems.

Requirements

Proven experience in Machine Learning and/or Applied Science.
Strong background in statistical inference and machine learning.
Bachelor's or Master's degree in Statistics, Mathematics, Physics, or Computer Science with a focus on machine learning.
Proven experience in applied machine learning, with a deep understanding of statistical inference and predictive modeling.
Demonstrated practical experience with deep learning techniques, particularly transformer-based models.
Strong track record of implementing research papers in machine learning or related fields.
Hands-on experience with Python (including libraries like PyTorch/TensorFlow) and SQL.
Ability to work independently and take ownership of projects.
Proactive approach to identifying key leverage points for data products.
Proficiency in modern machine learning techniques such as Model Evaluation and Validation, Deep Learning and Time Series Analysis, Logistic Regression, Naive Bayes, and Tree based Models (e.g., Random Forest).
Confidence to prioritize work and deliver demonstrable results on a tight cadence.
Demonstrated experience or understanding of the financial industry, especially in building and scaling FinTech products.

Nice To Haves

Curiosity and optimism, with a desire to understand and change the world.

Responsibilities

Perform data research and analysis using proprietary and external datasets.
Develop and validate models to achieve business objectives like growth, fraud mitigation, and risk control.
Iterate on existing and new models based on team feedback and real-world performance.
Collaborate with data engineers and product managers to translate work into production-grade, scalable data products.
Present findings and communicate technical information to diverse audiences.
Contribute to the growth of the Applied Science and Machine Learning team and practice at Grid.