Senior Data Scientist

About Bluevine

Bluevine is on a mission to enable a better financial future for small business owners through innovative banking solutions designed just for them. By combining best-in-class technology with advanced security and a deep understanding of the small business community, we deliver end-to-end banking and lending products that empower always-on entrepreneurs to grow their businesses with confidence. 

As a dynamic company with massive potential, we’re backed by leading investors such as Lightspeed Venture Partners, Menlo Ventures, 83North, Citi Ventures, and nearly 9 years of proven success. Since launching in 2013, we have grown exponentially, amassing over 400,000 customers across all 50 states and a global team of more than 500 people. Our passion is driven by purpose: to give small businesses the tools they need to succeed and we’re just getting started. 

All of this begins with our team who are driven by collaboration, problem-solving, and learning and growing together. With a commitment to innovation and community impact, our mission is to help every small business—and every team member—thrive. Join us!

This is a hybrid role. At Bluevine, we pride ourselves on our collaborative culture, which we believe is best maintained through in-person interactions and a vibrant office environment. All of our offices have reopened in accordance with local guidelines, and are following a hybrid model. In-office days will be determined by location and discipline.

ROLE PURPOSE:

Bluevine is looking for an exceptional Data Scientist to join our Redwood City or Austin office. The ideal candidate is a data enthusiast with a proven track record of solving complex data-related problems, using advanced statistical and machine learning tools, in a real-world product development setting. Candidates should demonstrate strong statistical, mathematical and technical skills, with proven capabilities to transition ideas into fully working projects. In addition, the candidate needs to have a creative and out of the box mindset, in order to examine our current processes and help us reach the next level of Data Science.

KEY ACCOUNTABILITIES (maximum 10)

1. Apply advanced statistical and predictive modeling techniques to build, maintain, and improve multiple real-time decision-making systems – from research to full production deployment
2. Collaborate on highly technical MLOps projects - from redesigning working infrastructures, maintaining and improving our current models and underlying systems and integrating new technologies into the data science workflow
3. Lead initiatives to define and implement  data collection procedures to include information that is relevant for building our analytical systems
4. Work closely with your fellow Data Scientists, the R&D department, and cross functionally with teams based across the US and Israel

KNOWLEDGE, SKILLS & ATTRIBUTES

Essential:

1. Undergraduate Degree in Statistics, Computer Science, Engineering or related quantitative field
2. 5+ years of work experience as a full-stack Data Scientist or a combination of advanced education and relevant work experience
3. Experience developing and deploying scalable Production Level code in Python, and working with ML frameworks such as PyTorch, Tensorflow and Scikit-Learn
4. Experience working with Relational Databases and writing optimized SQL queries
5. Theoretical and Practical experience developing and deploying ML models (spanning Ideation, R&D, Analysis, Training/Testing, Deployment and Measurement)

Desirable:

1. Master’s Degree or PhD in Statistics, Computer Science, Engineering or related quantitative field
2. Experience developing, deploying and maintaining Airflow DAGS 
3. MLOps Experience in the AWS Ecosystem
4. Designing and Running Experiments to measure impact
5. Domain level experience in Banking, Finance and Marketing

DECISION MAKING AND INFLUENCE

1. Experience working cross-functionally with Data Eng, MLOPS, Analytics, Business and Product partners
2. Experience communicating technical concepts and findings to a non-technical audienc