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.