Senior Data Science Product Engineer

AppOmni prevents SaaS data breaches by delivering end-to-end SaaS security. Our platform gives security teams clear visibility into posture, access, third-party connections, AI-related activity, and with built-in discovery to identify unsanctioned SaaS and Shadow AI tools. Backed by continuous monitoring and real-time threat detection, AppOmni helps enterprises identify and resolve risks early, keeping their SaaS applications secure.

Recognized as a Frost Radar™ 2025 Leader and Great Place To Work ® , AppOmni continues to set the standard for innovation and customer value in SaaS security. The largest and fastest-growing global enterprises across industries trust AppOmni to secure their SaaS applications.

About the Role

The Senior Data Science Product Engineer plays a key role in the company’s AI strategy. This role offers the opportunity to make a meaningful impact across the whole platform. The Senior Data Science Product Engineer is a hybrid position covering  the fronts of technical implementation, technical leadership and product management. This position focuses on envisioning, leading, managing, designing, deploying, and maintaining Data Science and Machine Learning systems , focusing on product roadmap ownership, high-level architecture and practical development, and hands-on implementation.

What You’ll Do

• Technical Leadership: Leading development efforts, mentoring engineers and product managers, and making key architectural decisions that involve Data Science, Machine Learning and AI.
• Cross-functional collaboration: Partner with Sales, Marketing, Customer Support and other departments across the organization for a full end to end ownership from the product and technical perspective as well as internal enablement and customer support.
• Develop greenfield projects and implement proof of concepts, including hands-on coding and connection to the product vision.
• Architect end-to-end Data Science and Machine Learning systems by choosing the best  implementation approach out of the holistic AI available tools (Data Science, Statistics, Machine Learning and Generative AI) for the problem to be solved, considering all relevant trade-offs and risks, while maximizing add-on value and minimizing costs, and work hands-on directly in the code with other engineers.
• Manage and lead the UX and UI development for the visualization aspects, Use data-driven solutions to address complex cybersecurity problems.
• Be responsible for the pipeline metrics and work hands-on with MLOps to optimize model and pipeline performance through proper monitoring, data mining, fine-tuning, prompt engineering, and hyperparameter tuning to meet latency and cost requirements.
• Drive technical strategy by evaluating third-party tools versus building in-house solutions to maximize ROI.
• Establish data governance and security standards to ensure the ethical