Job Summary

Startup Inno is seeking a highly accomplished Principal Data Scientist specializing in MLOps and Model Governance to lead the design, deployment, and oversight of enterprise-scale machine learning systems. This strategic role will drive the operational excellence, reliability, and compliance of AI/ML models across the organization. The ideal candidate will combine deep technical expertise with strong leadership skills to establish best practices in model lifecycle management, monitoring, and governance while enabling scalable AI innovation.

Key Responsibilities

• Lead the end-to-end MLOps strategy, including model deployment, monitoring, versioning, and lifecycle management.

• Establish and enforce model governance frameworks to ensure regulatory compliance, fairness, transparency, and auditability.

• Design scalable ML pipelines and CI/CD workflows for production-grade machine learning systems.

• Collaborate with data science, engineering, risk, and product teams to operationalize models efficiently.

• Implement robust model performance monitoring, drift detection, and automated retraining pipelines.

• Define best practices for reproducibility, documentation, and model validation standards.

• Mentor and guide senior data scientists and ML engineers across the organization.

• Evaluate and integrate modern MLOps tools and cloud-native AI platforms.

• Partner with leadership to align AI initiatives with business strategy and risk controls.

Required Skills and Qualifications

• Advanced degree (Masters or PhD preferred) in Data Science, Computer Science, Statistics, Machine Learning, or a related field.

• Strong expertise in MLOps frameworks, model governance, and production ML systems.

• Proficiency in Python and ML libraries such as TensorFlow, PyTorch, or Scikit-learn.

• Hands-on experience with CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes).

• Experience with cloud platforms such as AWS, Azure, or GCP.

• Solid understanding of model risk management, explainability, and responsible AI principles.

• Excellent stakeholder management and technical leadership skills.

Experience

• 10+ years of experience in data science, machine learning, or AI engineering.

• 5+ years leading production ML or MLOps initiatives at scale.

• Proven track record implementing model governance in regulated or enterprise environments.

• Experience mentoring senior technical teams and driving cross-functional programs.

Working Hours

• Full-time position.

• Flexible work schedule with core collaboration hours.

• Hybrid or remote options available based on location and business needs.

Knowledge, Skills, and Abilities

• Deep understanding of the ML lifecycle and production challenges.

• Strong architectural thinking for scalable AI platforms.

• Ability to balance innovation with risk and compliance requirements.

• Excellent problem-solving and decision-making capabilities.

• Strong communication skills with the ability to influence executive stakeholders.

• High attention to detail and commitment to quality and reliability.

Benefits

• Competitive executive-level compensation package.

• Performance-based bonuses and equity opportunities.

• Comprehensive health and wellness benefits.

• Flexible work arrangements and remote-friendly culture.

• Professional development and conference sponsorship.

• Generous paid time off and holidays.

Why Join

At Startup Inno, you will shape the future of responsible AI at scale. This is an opportunity to build world-class MLOps and governance capabilities from the ground up while working with cutting-edge technologies and high-impact business problems. You will collaborate with top-tier talent in an innovation-driven environment that values ownership, technical excellence, and continuous learning.

How to Apply

Interested candidates should submit their updated resume and a brief cover letter highlighting relevant MLOps and model governance experience. Qualified applicants will be contacted for an initial screening followed by technical and leadership interviews.