Staff Data Scientist

at  Credit Karma

Intuit Credit Karma is a mission-driven company, focused on championing financial progress for our more than 130 million members globally. While we’re best known for pioneering free credit scores, our members turn to us for everything related to their financial goals, including identity monitoring, applying for credit cards, shopping for insurance and loans (car, home and personal) and savings accounts and checking accounts* - all for free. Credit Karma has grown significantly through the years: we now have more than 1,700 employees across our offices in Oakland, Charlotte, Culver City, San Diego, London and New York City.

• Banking services provided by MVB Bank, Inc., Member FDIC

Join Credit Karma as a Staff Data Scientist - Data Science Ops, where you’ll champion operational excellence across organizations, driving AI-powered innovation and collaboration. With a deep understanding of data science and machine learning workflows, you’ll build and lead by example, fostering a culture of operational excellence. This highly cross-functional role requires strong partnerships with engineering, product, business, finance, and analytics teams, as well as mentorship skills, accountability, and the ability to navigate fast-paced environments with agility and poise.

PREFERRED QUALIFICATIONS:

• Strong programming skills in SQL, Python, Ruby, and Bash, with a solid understanding of Linux and experience with frameworks like scikit-learn, Keras, PyTorch, and TensorFlow
• Proficiency with DS / ML Ops frameworks like Airflow, Kubeflow, MLFlow or similar
• Cloud solution design and implementation expertise, with experience building DS / ML Ops pipelines on GCP

• Design and maintain systems, monitoring and alerting to detect anomalies and performance degradation of ML models; implement automation to streamline workflows
• Support with model development and deployment to production, ensuring seamless integration with existing infrastructure
• Develop and maintain dashboards and reports to provide insights into model performance and data quality
• Investigate and resolve model-related incidents, including data quality issues and prediction errors
• Design and implement cloud solutions (GCP) to containerize, deploy, version, and monitor data science models
• Collaborate and communicate effectively with data scientists, engineers and architects on model validation, testing, and quality assurance
• Document processes and best practices, and lead training sessions to upskill data scientists in Data Science Ops, promoting ops excellence and continuous learning