Manager Engineering

Job Summary

The Engineering Manager - Data Science is responsible for leading, coaching, and developing multiple pods of data scientists and machine learning engineers while ensuring high-quality, timely delivery of data science products and platforms. This role partners closely with AI product owners, AI architects, platform teams, and business stakeholders to ensure data science solutions are technically sound, scalable, and aligned to strategic outcomes.

Responsibilities

People Leadership & Talent Development

• Lead, coach, and develop a team of data scientists and ML engineers
• Set clear performance expectations and support career development
• Foster an inclusive, high-performing team culture

Technical & Engineering Leadership

• Provide technical guidance across data science and ML solutions working closely with AI Architects
• Ensure adherence to engineering standards and MLOps practices
• Review model designs and deployment strategies

Delivery & Execution Management

• Own delivery execution and capacity planning
• Ensure predictable, high-quality delivery
• Manage risks, dependencies, and technical debt

Stakeholder Collaboration

• Partner with AI Product Owners and business stakeholders
• Communicate progress, risks, and outcomes clearly

Quality, Governance & Responsible AI

• Ensure solutions meet quality, security, and compliance standards
• Uphold responsible AI principles

Degree Requirement

Bachelor s degree in engineering, computer science, data science, or a related field (or equivalent practical experience). Post Graduate or higher is desired.

Skills & Competencies

Engineering Management

Demonstrated ability to lead and develop high-performing technical teams delivering complex data science and machine learning solutions. Provides clear direction, sets expectations, and holds teams accountable for outcomes while supporting individual growth and engagement. Balances people leadership, technical oversight, and delivery execution in a fast-moving environment.

Data Science & Machine Learning

Strong working knowledge of data science, machine learning, GenAI and statistical modeling concepts, including supervised and unsupervised learning, feature engineering, model evaluation, and experimentation. Able to guide and review technical approaches, challenge assumptions, and ensure solutions are fit for purpose without requiring hands-on model development as a primary responsibility.

Delivery & Execution

Proven ability to plan, prioritize, and execute work in Agile or hybrid delivery environments. Manages team capacity, dependencies, and risks to ensure predictable delivery of high-quality outcomes. Uses data and metrics to assess progress, identify issues early, and drive continuous improvement.

Communication & Collaboration

Excellent verbal and written communication skills, with the ability to clearly articulate complex technical concepts to non-technical audiences. Builds strong partnerships with product owners, architects, platform teams, and business stakeholders to align expectations, manage trade-offs, and deliver value.

Software Development Life Cycle

Demonstrated knowledge of software and data science development life cycles, including experimentation, prototyping, production deployment, and ongoing operations. Understands the differences between research-oriented work and production-grade solutions and guides teams accordingly.

• Works within formal delivery and governance methodologies
• Understands phases, activities, dependencies, and key decision points across the life cycle
• Identifies common risks and quality considerations at each stage
• Interprets and guides technical documentation, design artifacts, and delivery plans

Artificial Intelligence

Working knowledge of artificial intelligence, GenAI and machine learning concepts, risks, and opportunities, with the ability to guide teams toward solutions that deliver business outcomes responsibly.

• Understands AI methodologies, capabilities, and limitations
• Guides teams in applying responsible AI principles, including fairness, transparency, robustness, and governance
• Keeps informed of emerging AI technologies and evaluates their applicability to business needs
• Supports the adoption of AI practices that balance innovation with risk management

Programming & Technical Literacy

Strong technical literacy across programming and data science tools sufficient to effectively review code, designs, and technical approaches.

• Able to assess code quality, maintainability, and architectural alignment
• Collaborates effectively with hands-on engineers and data scientists
• Understands testing, version control, and deployment concepts
• Hands-on coding is not a primary responsibility, but technical depth is sufficient to guide and challenge solutions

Technical Troubleshooting

Demonstrated ability to lead teams through complex technical and operational issues across data, models, and production systems.

• Guides investigation and resolution of data, model, and system issues
• Reviews logs, metrics, and system behavior to support root cause analysis
• Partners with platform and operations teams to resolve production incidents
• Ensures issues are documented, learnings are captured, and preventative actions are implemented

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.