11 Grid Data Scientist Jobs Hiring Near You

• 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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.