Senior Data Scientist

About Bluevine

Bluevine is transforming small business banking with innovative solutions like checking, lending, and credit—all tailored to help entrepreneurs thrive. With best-in-class technology, advanced security, and a deep understanding of the small business community, we’re empowering entrepreneurs to grow with confidence.

Backed by leading investors like Lightspeed Venture Partners, Menlo Ventures, 83North, and Citi Ventures, we’ve been supporting SMBs since 2013, serving over 750,000 customers nationwide and growing a dynamic global team of 500 people. Our mission? To fuel small businesses with the financial tools they need to succeed.

At Bluevine, you’ll be part of a collaborative, fast-paced team that’s reshaping the future of banking. Ready to make an impact?

About the Role:

We are building a new, cutting-edge data science team in India and are looking for an experienced Senior Data Scientist to join us. This role is an exciting opportunity to apply your expertise in advanced statistical techniques, machine learning, and predictive modeling to solve complex business problems. You will be instrumental in shaping the data science function, developing scalable models, and collaborating with teams across the globe.

What Youll Do:

• Advanced Modeling & Analytics: Utilize advanced statistical, machine learning, and predictive modeling techniques to design, build, and improve decision-making systems that drive business outcomes.
• Scalable Model Implementation: Apply modern software engineering practices to implement machine learning models in a scalable, robust, and maintainable manner. Ensure models are production-ready and optimized for performance.
• MLOps & System Integration: Lead technical MLOps projects including the redesign of existing infrastructures, maintaining and improving current models and systems, and integrating the latest technologies into our data science workflows.
• Data Collection & Management: Manage and lead initiatives to design and implement robust data collection procedures necessary for building comprehensive analytical systems.
• Collaboration & Leadership: Work closely with Data Scientists, the R&D department, and cross-functional teams across India, the US, and Israel to drive data science initiatives. Provide mentorship to junior data scientists and contribute to the growth of the team.
  
  

• 5+ Years of Experience: A minimum of 5 years of professional experience as a Data Scientist, with a proven track record of developing and deploying machine learning models in a production environment.
• Proficiency in Python: Extensive experience in developing and deploying scalable production-level code in Python, with a deep understanding of machine learning libraries such as Scikit-learn, PyTorch, TensorFlow, or similar.
• Database Expertise: Strong experience working with relational databases, writing optimized SQL queries, and managing large datasets.
• End-to-End ML Lifecycle: Comprehensive experience in the full lifecycle of ML model development, including ideation, training/testing, deployment, and monitoring in a production setting.
• Data Analysis & Insight Generation: Ability to analyze complex datasets and extract actionable insights that inform business decisions.
• Educational Background: A Masters or Ph.D. in Statistics, Computer Science, Engineering, or a related quantitative discipline.
• Communication Skills: Proven ability to communicate complex technical concepts and findings to non-technical stakeholders, with a focus on clear and actionable insights.
  
  

• Cross-Functional Experience: Experience working cross-functionally with Data/MLOps Engineering, Analytics, Business, and Product teams.
• Domain Expertise: Previous experience in specialized areas such as fraud detection, credit risk prediction, or graph models and analytics.
• MLOps & Cloud Experience: Hands-on experience with MLOps, particularly within the AWS ecosystem, and familiarity with tools like Apache Airflow.
• Experimental Design: Expertise in designing and executing experiments to measure model impact and effectiveness.
  
  

• Excellent group health coverage and life insurance
• Stock options
• Hybrid work model
• Meal allowance
• Transportation assistance (terms and conditions apply)
• Generous paid time off plan, Holidays
• Company-sponsored mental health benefits
• Financial advisory services for both short- and long-term goals
• Learning and development opportunities to support career growth
• Community-based volunteering opportunities