Key Responsibilities

• Drive Strategy & Vision: Partner with senior leadership, product management, and other stakeholders to define and execute the machine learning roadmap aligned with business goals.

• Team Leadership: Lead, mentor, and grow a team of ML scientists and engineers. Create an environment that encourages innovation, high performance, and career development.

• Technical Oversight: Guide and review the development of machine learning models and data pipelines, ensuring scalability, scientific validity, and production readiness.

• Cross-Functional Collaboration: Collaborate with Product and Analytics teams to scope projects, prioritize initiatives, and ensure seamless data and model integration.

• Operational Excellence: Define project goals, success metrics, and delivery timelines. Ensure effective resource allocation and continuous improvement in team processes.

• Foster Scientific Rigor: Uphold high standards of experimentation, measurement, and peer review. Promote the use of reproducible research and robust evaluation practices.

• Data Strategy: Partner with the operations and data teams to ensure access to high-quality labeled data, and proactively shape data acquisition strategies where needed.

• Talent Development: Provide coaching and technical guidance to team members. Conduct performance reviews, identify skill gaps, and invest in team capability building.

• Hiring: Attract, interview, and hire top talent to grow the team and enhance its diversity of thought and background.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field with 6+ years of relevant industry experience, including 3+ years in people management.

• Proven experience leading machine learning teams in an applied industrial setting.

• Deep understanding of modern ML approaches including classification, regression, NLP, clustering, deep learning, and/or reinforcement learning.

• Strong programming background in Python, Java, or similar, with exposure to production-grade ML systems.

• Proficiency with big data processing frameworks such as Hadoop, Spark, and SQL.

• Excellent communication, storytelling, and stakeholder management skills.

• Demonstrated ability to translate business needs into scientific problems and to prioritize for impact.
   

Additional Qualifications

• Master’s or Ph.D. in a relevant field (Computer Science, ML, Stats, etc.)

• Track record of impactful publications and/or patents in machine learning or related areas.

• Contributions to open-source ML tools or frameworks.

• Experience with modern large language models, graph-based ML, or knowledge graph construction.

  • Strong presence in scientific communities through talks, panels, or organizing roles.