Staff Applied Scientist

About us Today's financial system is built to favor those with money. Grid's mission is to level that playing field by building financial products that help users better manage their financial future. The Grid app lets users access cash, build credit, spend money, optimize their taxes, and lots, lots more. Grid is a fast-growing team that's deeply passionate about making a difference in the lives of millions. We're solving huge problems and believe that every team member has a big role to play. Come join our growing team in our brand new Seattle office! The role We're adding an Applied Scientist to our team to help us build and scale our core product lines. You'll work closely with product, engineering and business leaders to make a difference with data. With access to multiple robust datasets and clear research objectives, you'll have a significant impact on Grid's progress as a business—as well as our users' happiness and success. Projects will include fraud detection, prevention and mitigation in novel arenas, such as risk underwriting for various lending/advance programs; predictive analytics to drive our payout and repayments systems; and more. The team We're focused on serving our users and building a robust product and business above all else. To this end, Grid's team members experience high levels autonomy and ownership, and as a company we value curiosity, learning and growth. As an Applied Scientist, you'll have an opportunity not only to identify key leverage points for our data products, but also to set the standard for Grid's statistical inference and machine learning practice. The tech stack Our backend tech stack is based on Python, GCP, Go, protobufs, BigQuery and MySQL. We have built our platform from the ground up to optimize for clean data sources, and we have made numerous investments into data warehousing, streaming analytics infrastructure and offline data cleanliness. As a result, we think Grid is positioned for efficient and powerful applied science. 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 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. #J-18808-Ljbffr Grid