Technology Leader - Data Science (1541)

Overview: 

The Technology Leader - Data Science is a management level role that will drive Prodege’s experimentation, business intelligence reporting, modeling, and machine learning capabilities to power ranking, pricing, and personalization at scale. This role is integral to ensuring that business goals translate into measurable and actionable data science outcomes, delivering models that directly impact revenue growth, user engagement, and overall platform optimization. By combining deep technical expertise with a strong product and business mindset, the Head of Data Science will elevate experimentation rigor, causal inference frameworks, and applied machine learning across the organization—ultimately ensuring Prodege’s data-driven advantage in the marketplace.

Prodege:

A cutting-edge marketing and consumer insights platform, Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands, marketers, and agencies uncover the answers to their business questions, acquire new customers, increase revenue, and drive brand loyalty & product adoption. Bolstered by a major investment by Great Hill Partners in Q4 2021 and strategic acquisitions of Pollfish, BitBurst & AdGate Media in 2022, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences.

As an organization, we go the extra mile to “Create Rewarding Moments” every day for our partners, consumers, and team. Come join us today!

***The ideal candidate will be located in the Southern Ca area and be available to report to the El Segundo Headquarter's office two to three days per week. Prodege is open to visa sponsorship for this role.***

Primary Objectives: 

• Experimentation Leadership: Own A/B testing frameworks, uplift modeling, and causal inference methods to rigorously evaluate the true impact of models.
• Applied ML Deployment: Lead the design, development, and productionization of machine learning models for ranking, pricing, and personalization.
• BI Reporting & Insights: Oversee the design, delivery, and continuous improvement of business intelligence reporting and dashboards, ensuring leaders across the company have timely, accurate, and actionable insights to inform strategic and operational decisions.
• Feature Innovation: Guide feature engineering efforts from event-level auction data and user engagement signals to maximize model performance.
• Strategic Translation: Convert business objectives into experimentation plans, model KPIs, and measurable outcomes that drive P&L impact.
• Cross-Functional Collaboration: Partner with Product, Engineering, and Analytics leaders to embed data science into business decision-making.
• Team Development: Lead , mentor, and scale a high-performing data science team, fostering innovation, technical excellence, and business alignment.

Qualifications - To perform this job successfully, an individual must be able to perform each job duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Detailed Job Duties:

• Define and oversee experimentation frameworks, ensuring models are rigorously validated against business KPIs.
• Lead development and productionization of machine learning models and BI reporting for ranking, pricing, and personalization.
• Design and oversee the delivery of scalable BI reporting solutions (e.g., dashboards, automated reports) that provide actionable insights to business leaders.
• Ensure BI reporting integrates seamlessly with experimentation and modeling outputs to support executive decision-making, P&L management, and revenue growth initiatives.
• Drive feature engineering innovation from event-level auction and engagement data.
• Partner with Product and Engineering to design systems that enable real-time experimentation and model deployment.
• Establish data science standards, processes, and best practices across experimentation and mo