Manager, Data Science

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.

About the Role:

We are establishing a pioneering data science team in India and are seeking a highly skilled Data Science Manager to drive our efforts. In this role, you will lead a team of data scientists, guiding their work while also remaining hands-on with advanced data science tasks. You will play a crucial role in developing and implementing sophisticated models, coordinating with the group lead, and ensuring alignment with our strategic goals.

Key Responsibilities:

• Team Leadership & Development: Lead and mentor a team of 2 individual contributors (ICs), providing guidance on project execution, technical challenges, and professional growth. Foster a collaborative and innovative team environment.
• Hands-on Modeling & Analytics: Apply your expertise in advanced statistical, machine learning, and predictive modeling techniques to design, build, and improve decision-making systems. Set the standard for technical excellence within the team.
• Project Management: Manage and oversee data science projects from concept to deployment, ensuring timely delivery of high-quality solutions. Allocate resources effectively and prioritize tasks to align with business objectives.
• Stakeholder Communication: Serve as the primary point of contact between your team and the group lead. Communicate progress, challenges, and insights clearly and effectively, ensuring alignment with broader organizational goals.
• MLOps & System Integration: Lead efforts to maintain and enhance the data science infrastructure, integrating new technologies and practices into our workflows. Ensure that models are robust, scalable, and maintainable.
• Strategic Data Collection: Oversee initiatives to develop and implement data collection strategies that support advanced analytics and model development. Ensure that data pipelines are efficient and reliable.

What We’re Looking For:

• 7+ Years of Experience: A minimum of 7 years of experience in data science, with at least 2 years in a leadership or managerial role. Demonstrated experience in leading teams and delivering complex data-driven solutions.
• Expertise in Python: Extensive experience in developing and deploying production-level code in Python, with a deep understanding of machine learning frameworks such as Scikit-learn, PyTorch, TensorFlow, or similar.
• Strong SQL and Database Knowledge: Proficiency in working with relational databases, writing optimized SQL queries, and managing large-scale datasets.
• Comprehensive ML Lifecycle Experience: Proven ability to manage the entire lifecycle of ML model development, from ideation and experimentation to deployment and monitoring in a production environment.
• Analytical & Strategic Thinking: Ability to analyze complex data sets, derive actionable insights, and communicate these insights effectively to both technical and non-technical stakeholders.
• Educational Background: A Master’s or Ph.D. in Statistics, Computer Science, Engineering, or a related quantitative field is preferred.
• Proven Leadership Skills: Demonstrated ability to lead a team, manage multiple priorities, and work effectively across functions and geographies.

Bonus Points:

• Cross-Functional Collaboration: Experience working with Data/MLOps Engineering, Analytics, Business, and Product teams in a collaborative environment.
• Domain Specialization: Expertise in areas like fraud detection, credit risk prediction, or graph models and analytics.
• MLOps & Cloud Infrastructure: Hands-on experience with MLOps, particularly within the AWS ecosystem, and familiarity with orchestration tools like Apache Airflow.
• Experimentation & Impact Analysis: Skilled in designing experiments and analyzing their outcomes to measure the impact and effectiveness of data-driven solutions.