Director Data Science

We are seeking an experienced Director of Data Science with 14+ years of expertise in the data science and AI ecosystem to lead, scale, and mentor our high-performing team of data scientists and machine learning engineers. In this executive leadership role, you will bridge the gap between technical innovation and business strategy. You will serve as a key client-facing partner, owning strategic account relationships, translating complex business challenges into cutting-edge AI/ML solutions, and providing hands-on technical guidance to your engineering teams.

Key Responsibilities

Strategic & Client Leadership

• Client & Stakeholder Management: Act as the primary technical interface for executive client stakeholders. Understand their core business imperatives, present complex AI/ML strategies, and demonstrate measurable ROI from data science initiatives.
• Solution Architecture & Strategy: Drive the long-term vision for data science and AI adoption across key accounts, translating business problems into high-impact statistical, machine learning, and generative AI solutions.
• Business Growth: Partner with cross-functional business development and product leadership teams to identify new data opportunities, define proposals, and participate in client pitches.

Team Management & Mentorship

• People Leadership: Manage, recruit, and retain a multi-disciplinary team of Senior Data Scientists, Machine Learning Engineers, and AI Researchers.
• Technical Guidance: Conduct deep-dive technical reviews, architectural assessments, and model design audits to ensure production reliability, scalability, and fairness.
• Talent Development: Foster a culture of continuous learning, mentoring junior and senior staff on advanced algorithms, production ML engineering practices, and executive communication.

Technical Excellence & Operations

• End-to-End Execution: Oversee the lifecycle of data products from discovery, exploratory data analysis (EDA), prototyping, to production deployment (MLOps) and monitoring.
• Best Practices & Governance: Establish enterprise-grade frameworks for model governance, ethical AI practices, data quality assurance, and code reproducibility.
• R&D & Innovation: Stay ahead of industry trends, evaluating and integrating emerging technologies in Generative AI, Large Language Models (LLMs), deep learning, and scalable cloud ML platforms.