Senior Product Manager, Custom Algorithms

Cognitiv · New York, NY | Bellevue, WA | San Mateo, CA

  • Senior
  • Full-time
  • $175,000 – $210,000
  • Posted 2026-09-17
  • Confirmed live on 25 September 2026

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Job description

Are you ready to revolutionize the advertising industry?

At Cognitiv, we are not just another AdTech company—we are industry trailblazers redefining media buying with our Deep Learning Advertising Platform. Since 2015, we have harnessed the power of cutting-edge deep learning technology and data science to transform how brands connect with their customers. Our mission? To bring intelligence to advertising and deliver unparalleled precision, relevance, and impact at scale.

With our innovative platform, advertisers enjoy unprecedented flexibility—whether it is activating Dynamic Deals through their preferred DSP, leveraging our managed service DSP, or utilizing our industry-first ContextGPT product. As a part of Cognitiv, you will be at the forefront of AI-driven advertising solutions, driving change and achieving remarkable growth in a rapidly evolving industry.

Now, we’re growing!

The Role

This role is accountable for the growth, strategic direction and performance of Cognitiv's Custom Algorithms product by defining what the market needs from it, which KPIs it optimizes toward, and how we drive adoption and revenue growth. You will work closely with Data Science, Engineering, Sales, Customer Success, and Product Marketing to deliver new products and capabilities that drive top-line revenue growth and customer retention.

Location: This position will be located in NYC, San Mateo, or Bellevue with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote optional (Thursday/Friday).

What You’ll Do:

• End-to-End Strategy & Roadmap Ownership. Lead product strategy for Custom Algorithms from vision to execution. Prioritize high-impact features, evaluate incoming custom client requests strategically to determine if they are worth pursuing, and make the hard calls on what to ship, defer, or decline to keep the team focused.

• Data Science & Engineering Partnership. Set the product goals models optimize toward, and agree on the quality bar a model must clear before launch with Data Science and Engineering. Build deep trust and credibility with the Data Science team while ensuring clear boundaries: Data Science owns the modeling approach, features, and technical execution; you own what good looks like and the product direction.

• Performance Accountability. Monitor and analyze campaign performance to ensure algorithms perform at a high level. When campaigns underperform, size the commercial impact and drive prioritization with Science and Engineering through to resolution.

• Adoption and Commercial Fit. Partner directly with Sales, Customer Success, and Product Marketing to drive uptake across the advertiser base. Support Sales on strategic client calls when deep product expertise is needed. Define which advertisers are a good fit, set clear performance expectations before a campaign launches, and know when to decline.

• Scalable Delivery & Process. Drive down the manual effort and time required to launch each custom model, working with Data Science and Engineering to establish a repeatable, scalable process for running custom algorithms.

• Market Analysis and Revenue Growth. Conduct market research to understand industry trends, evaluate how our product stacks up against competitors, and uncover new opportunities. Identify which types of customers we should be going after and how, determine which new KPI models to build, uncover new opportunities, and expand top-line growth.

• Cross-Functional Leadership. Own the product point of view for Custom Algorithms across Data Science, Engineering, Sales, Customer Success, Product Marketing, and executive leadership.

Who you are:

• AI & ML Product Fluency. You've worked on products where Machine Learning and/or Deep Learning were the core of the value, so you know how they behave: data requirements, probabilistic output, performance that drifts. You keep current on what's becoming possible in AI and what it means for products like ours. You can judge whether a model is serving the product goal, push back when it isn't, and be taken seriously by the people who build it.

• Commercial ML Track Record. You've owned ML products where the output was money, not a metric (e.g., driving measurable revenue growth). Recommendations, pricing, risk, fraud, search ranking, marketplace matching. High-volume inference, noisy feedback, and a real commercial consequence when the model is wrong.

• Technical Translator. You translate model behavior into commercial language. You can explain to an advertiser why their conversion volume isn't enough to train on, and tell a sales lead what "good" looks like before the deal is signed. This is the highest-leverage skill in the role.

• Analytically Self-Sufficient. You pull and interrogate performance data yourself. Given an underperforming campaign, you arrive with a hypothesis, not a request for someone else to look into it.

• Accountable Product Owner. You've owned a technical product end to end (or ow

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