Director, Data Science

About Keystone AI… Keystone AI is redefining IT operations for the modern enterprise. Our AIOps platform empowers organizations to achieve Autonomic IT — where systems are self-healing, self-optimizing, and seamlessly aligned with business outcomes. We help enterprises and service providers gain unified visibility across hybrid and multi-cloud environments, automate workflows, and unlock performance at scale. We’re accelerating digital transformation through the power of automation, AI, and analytics — giving IT and business leaders the tools to deliver superior customer experiences, drive efficiency, and innovate with confidence. We are seeking a hands-on Director of Data Science to provide the technical leadership, organizational alignment, and product-oriented execution needed to advance our AI strategy. This leader will help mature the Data Science function, clarify ownership, and ensure AI and machine learning investments translate into reliable, meaningful product outcomes. You will spend roughly 60% of your time hands-on: architecting solutions, shaping agentic AI and ML platforms, defining technical standards, and solving complex problems. You will spend roughly 40% of your time leading a small, high-performing team: coaching, prioritizing, hiring, performance management, and building a more sustainable operating model. This role will partner closely with Product, Engineering, UX, and senior leadership to align Data Science priorities with business strategy, customer needs, and user experience. If you are energized by deep technical work, team leadership, and creating stronger alignment across functions, this role is for you. What you’ll be doing… Technical Leadership & Delivery: Architect complex data science, machine learning, and agentic AI systems end to end, from foundational capabilities through production deployment. Personally build, validate, and deploy high-complexity predictive models and machine learning solutions that solve core business and customer problems. Define enterprise-level best practices for AI/ML systems, experimentation, governance, model lifecycle management, observability, and responsible AI. Bring technical leadership and decision ownership to ambiguous problems, helping the team clarify what to build, why it matters, and how success will be measured. Establish practical guardrails for AI capabilities so user expectations are aligned with what can be delivered consistently, reliably, and with excellence. Cross-Team Influence & Architecture: Partner with Product, Engineering, UX, and senior leadership to ensure Data Science work is integrated into the broader product strategy and roadmap. Clarify the Data Science charter across research, modeling, productization, and execution, ensuring the function is focused on the highest-value priorities. Translate business needs, customer problems, and product goals into a coherent Data Science agenda. Architect shared AI capabilities, frameworks, and standards that other teams can confidently build against and consume. Improve collaboration, documentation, handoffs, and end-to-end testing across Data Science, Product, and Engineering. Ensure AI and ML solutions are designed with the end user in mind, including how users will experience, trust, and act on the outcomes. Model Deployment & Monitoring: Partner with software engineering and DevOps teams to deploy models and AI capabilities into production environments. Monitor model performance over time, recalibrating, optimizing, and improving systems as needed. Design and implement A/B testing and experimentation frameworks to evaluate model effectiveness, user impact, and business value. Strengthen production readiness practices, including testing, documentation, monitoring, explainability, and performance measurement. Team Leadership & Growth: Lead, manage, and develop a small team of data scientists, owning their growth, performance, prioritization, and career development. Provide close technical guidance and management proximity to the work so the team has clear direction, coaching, and decision support. Reduce unnecessary coordination burden on individual contributors by creating clearer ownership, decision rights, and operating rhythms. Set technical and cultural direction across the team while influencing broader organizational capability. Mentor senior technical talent, raising the bar on rigor, collaboration, communication, and delivery. Grow the team through hiring, defining roles, raising the talent bar, and scaling the function as demand increases. Qualities you possess… 10 to 15 years of experience in data science, machine learning, AI systems, or a related field, including demonstrated technical leadership across multiple teams or initiatives. 3 to 5+ years leading and growing a data science, machine learning, or AI team, with a track record of mentoring and developing senior technical talent. Recognized depth in machine learning, AI systems, advanced analytics, or app