Engineering Manager (Data Science)

Role Overview

We are seeking an experiencedEngineering Manager – Data Science to lead a high-performing Data Science squad responsible for delivering scalable machine learning solutions, predictive models, and AI-driven products that drive business impact.

The ideal candidate combines deep technical expertise in Data Science and Machine Learning with strong people leadership capabilities. This role requires a strategic thinker who can manage a multidisciplinary squad, define technical roadmaps, mentor engineers and data scientists, and collaborate closely with Product, Engineering, Analytics, and Business stakeholders.

Key Responsibilities

Leadership & Team Management

• Lead, coach, and develop a squad of Data Scientists, Machine Learning Engineers.
• Drive team performance through goal setting, mentoring, career development, and continuous feedback.
• Build and foster a culture of innovation, ownership, collaboration, and technical excellence.
• Support recruitment, onboarding, and talent development initiatives.
• Manage squad capacity, planning, and execution to ensure successful delivery of strategic projects.

Technical Leadership

• Define and execute the Data Science roadmap aligned with business objectives.
• Lead the design, development, deployment, and monitoring of machine learning models and AI solutions.
• Establish engineering best practices for model development, experimentation, MLOps, and production deployment.
• Guide architectural decisions related to data platforms, machine learning infrastructure, and AI systems.
• Ensure scalability, reliability, and maintainability of Data Science solutions.

Stakeholder Management

• Partner with Product Managers, Engineering Leaders, and Business stakeholders to identify opportunities for data-driven decision making.
• Translate complex business problems into analytical and machine learning solutions.
• Communicate technical findings and recommendations to both technical and non-technical audiences.
• Drive alignment across cross-functional teams to deliver measurable business outcomes.

Delivery & Execution

• Oversee end-to-end project delivery from discovery and experimentation to production deployment.
• Monitor KPIs and model performance to ensure continuous improvement.
• Balance technical debt, innovation, and business priorities effectively.
• Drive agile delivery practices within the squad.

• 10+ years of experience in Data Science, Machine Learning, Software Engineering, or related technical fields.
• 4+ years of people management experience leading Data Science or Machine Learning teams.
• Proven experience leading cross-functional squads in product-driven environments.
• Demonstrated track record of deploying machine learning models into production.

Technical Skills

Strong expertise in:

• Machine Learning
• Statistical Modeling
• Predictive Analytics
• Deep Learning
• NLP and/or Generative AI
• Experiment Design and A/B Testing

Advanced proficiency in:

• Python
• SQL
• Data Visualization tools

Experience with:

• MLOps practices
• Model monitoring and governance
• Cloud platforms (Azure, AWS, or GCP)
• Data Engineering concepts and modern data platforms

Familiarity with:

• LLMs and Generative AI applications
• Feature stores
• Recommendation systems
• Real-time machine learning solutions

Preferred Qualifications

• Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.
• Experience within FinTech, Digital Products, E-commerce, Banking, or Technology organizations.
• Experience managing large-scale data platforms and AI initiatives.
• Knowledge of modern software engineering practices and DevOps methodologies.

• Lead cutting-edge AI and Data Science initiatives.
• Build products impacting millions of users.
• Work with highly talented engineering and product teams.

• Shape the future of data-driven decision making within the organization.
• When you come to our b_labs office, you'll find creative workspaces and an open design to foster collaboration between teams.
• You know best whether you want to work from home or in the office.
• From "Day 1" you will receive all the equipment you need be successful at work.