Role Overview We are seeking an experienced Engineering 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 ResponsibilitiesLeadership & 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.
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.
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.
