Senior Product Manager, Custom Algorithms
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 RoleThis 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 owned a significant, high-impact component of a complex technical product). Discovery through ship, with the tradeoff calls made on incomplete information. You write specs engineering can build from without follow-up clarification sessions. You're the accountable owner, not a coordinator.
- Credible in the Room. You present with authority to advertisers and to executives, and you can hold a room when the news isn't good.
- Proven Track Record. 5+ years in Product Management, or in data science / ML engineering with direct product ownership.
Bonus Points If You Have:
- Programmatic advertising experience, including bidding and auction mechanics, campaign KPIs, and attribution.
- Causal and experimental rigor. You can design a holdout, reason about selection effects, and tell whether a model is creating outcomes or just finding people who would have converted anyway.
- A hands-on modeling background, or you still write SQL and Python.
- Experience turning a bespoke, specialist-delivered offering into something repeatable and productized.
- Familiarity with signal loss and privacy, including first-party data onboarding, clean rooms, and identity.
- Enthusiasm for staying up to date with the latest AI technologies and trends and identifying ways to apply them to products.
Salary: $175,000 - $210,000 Base Salary + Equity
What We Offer
Compensation is based on experience, skills, and other factors. Base salary is just one part of your total rewards at Cognitiv—you’ll also receive equity and a comprehensive benefits package.
Highlights include:
- Medical, Dental and Vision plan for US employees & Extended Health Benefits for Canadian employees
- 12 weeks paid parental leave + 4 weeks WFH
- Unlimited PTO + Work-From-Anywhere August
- Career development with clear advancement paths
- Equity for all employees
- Hybrid work model & daily team lunch
- Health & wellness stipend + cell phone reimbursement
- 401(k) & RRSP with employer match
- Parking (CA, WA, Vancouver offices) & pre-tax commuter benefits
- Employee Assistance Program
- Comprehensive onboarding (Cognitiv University)
- …and more!
What You’ll Find at Cognitiv
- Festiv – We make work fun with cross-team games, events, and creative team bonding.
- Responsiv – You’ll be close to clients and leadership, influencing real outcomes.
- Inclusiv – Diversity and individuality are celebrated across all levels.
- Inventiv – We reward curiosity and embrace bold ideas.
- Transformativ – We support your growth with training, mentorship, and flexibility.
- Collaborativ – We operate across coasts, connected by purpose and teamwork.
Cognitiv is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive workplace for all.
Note on AI Use: Cognitiv may use AI technology to assist with certain administrative aspects of the hiring process, such as note-taking, interview documentation, and reporting. However, every resume and application is reviewed directly by our recruiting team. AI tools are used solely for operational support and do not influence candidate evaluation or hiring decisions.
About Cognitiv
Cognitiv uses proprietary Deep Learning technology to help companies with Big Data problems get beyond analysis to decisions.