Who We Are 

Cobalt was founded on the belief of a fundamental human aspiration: the desire to live better and safer. It all started in 2013, when our founders realized that pentesting can be better. Today our diverse, fully remote team is committed to helping organizations of all sizes with seamless, effective and collaborative Offensive Security Testing that empower organizations to OPERATE FEARLESSLY and INNOVATE SECURELY.

Our customers can start a pentest in as little as 24 hours and integrate with advanced development cycles thanks to the powerful combination of our SaaS platform coupled with an exclusive community of testers known as the Cobalt Core. Accepting just 5% of applicants, the Cobalt Core boasts over 400 closely vetted and highly skilled testers who jointly conduct thousands of tests each year and are at the forefront of identifying and helping remediate risk across a dynamically changing attack surface.

Cobalt is an Equal Opportunity Employer and we strive to build a diverse and inclusive workforce at our company. At Cobalt we aspire to engage with diverse individuals, communities, and organizations in order to continue to nurture our unique rich diverse culture. Join our team, and be your true self to do your best work.

Description

The Director of Data Science position is a pivotal leadership role responsible for overseeing all data science and advanced analytics initiatives across the enterprise. Reporting directly to Cobalt’s CTO, the director is responsible for guiding the conceptualization, development, and implementation of comprehensive data strategies that directly drive critical business decisions and align with overarching company objectives. 

The successful candidate is responsible for leading, inspiring, and empowering a multidisciplinary team of highly skilled data scientists and analysts from the ground up  - identifying strategic opportunities for leveraging data science and integrating data-driven solutions across the organization. 

As a lean-forward leader you’ll take a hands-on role in transforming raw, complex data into meaningful, actionable understandings that directly propel the company's growth, innovation, and competitive advantage in the world of penetration testing and AI.

What you’ll do

Strategic Direction & Roadmap Definition:

• Define, lead, and continuously refine the data science vision, overarching strategy, and multi-year roadmap, ensuring direct alignment with the organization's overarching business goals and clearly defined, measurable outcomes.   
• Serve as a principal strategic advisor and thought leader, proactively championing the ethical and responsible adoption and pervasive utilization of AI/ML technologies across all relevant organizational functions.   
• Collaborate intimately with cross-functional teams, including Product, Engineering, Marketing, and Sales, to systematically identify, evaluate, and prioritize high-impact data science opportunities.   

Model Development & Operational Oversight:

• Oversee the entire lifecycle of machine learning models, encompassing their conceptual design, rigorous development, comprehensive validation, robust production deployment, continuous monitoring, and iterative improvement.
• Drive the development of sophisticated copilot and AI pentester solutions.
• Ensure that the data science, engineering, and product management teams build reliable and accurate models, and rigorously selects and applies appropriate evaluation techniques to validate their performance.   

Data Governance, Quality & Compliance:

• Establish, implement, and promote industry-leading best practices in data collection, storage, analysis, data governance, and model reproducibility across all data science projects.
• Ensure stringent compliance with all applicable data privacy regulations, security standards (e.g., SOC 2, GDPR, CCPA, specific student data privacy requirements if relevant), and ethical guidelines for data utilization throughout the organization.
• Collaborate strategically with Data Engineering teams to design and implement scalable, well-defined data assets, robust feature stores, and precise metrics that support evolving analytical and product requirements.   

Communication & Stakeholder Engagement:

• Serve as a critical liaison and bridge between highly technical teams and business stakeholders, ensuring seamless alignment on strategic priorities, project deliverables, and the demonstrable strategic value of data science initiatives.
• Represent the organization effectively in external collaborations, industry conferences, and professional consortia to maintain a leading position at the forefront of data science advancements.