Principal Product Manager, Data and Recommendations

1 нед. назад

210k–240k USD / yearUSALeadHybrid

aimlapiproduct_management

Lead data, recommendations, and composable platform strategy focusing on AI-enabled recommendations and enterprise data.

Требования

8+ years of product management experience in B2B SaaS or DaaS, including at least 3 years at a staff or principal level.
Proven experience building and productizing AI/ML-powered recommendation systems, including model evaluation, quality measurement, and reinforcement learning.
3+ years experience in ad/martech or a data-heavy vertical.
Strong experience with data products or developer platforms.
Proven success taking technically complex products from strategy and architecture through delivery, adoption, and iteration.
Deep technical and product fluency across APIs, MCP tools, data models, and AI/ML products.
Familiarity with generative AI, large language models, agents, retrieval and grounding, evaluation methods, and probabilistic system behavior.
Aggressive use of AI tools to accelerate product work and experimental mindset on finding new ways to use AI to improve speed and quality.
Hands-on ability to perform lightweight data analysis using SQL, Python, and related tools to investigate product behavior, test hypotheses, and inform decisions.
Excellent communicator across functions and levels, from engineers to executives to external partners
Customer-first orientation—you translate discovery into action, and can handle feedback with empathy, urgency, and clarity
A calm, positive presence even in complexity—able to drive outcomes without drama

Условия

$210-$240k + Offers Equity
Please be aware that will only contact candidates through emails ending in @ .com . We will never ask for personal information, such as your Social Security number, bank account number, or password, through email. If you receive an email claiming to be from that does not come from a @ .com email address, or if the email asks for personal information, please do not respond and report the email to us at IT@.com

Другое

is the creative data company. Its scoring software and analytics have become an essential ingredient in the creative and media decisions of the world’s largest marketers and agencies, as they strive to drive business results through improved creative effectiveness. As the leader in creative data, ’s influence lies in its partnerships and integrations across the digital ad ecosystem, its dozens of proprietary models, and in operating the industry’s most robustly instrumented human-reinforcement learning model for creativity.
The Principal Product Manager will lead ’s data, recommendations, and composable platform strategy. This role will shape how turns creative and performance data into actionable guidance that makes that intelligence available wherever customers and partners need it through applications, APIs, MCP tools, and agents.
This is a technical product role spanning data products, AI-enabled recommendations, developer platforms, and partner integrations. It’s particularly important for candidates to have experience building recommendation systems driven by enterprise data and an array of AI/ML techniques.
We are an AI-first product organization, and every team member is expected to use AI tools directly to accelerate discovery, analysis, prototyping and delivery.
This role is based in our Midtown NYC office with periodic in-office presence averaging about 30% over any given month.
Own the product vision, roadmap, and delivery for ’s creative data, recommendation, API, MCP, and agent capabilities.
Advance ’s composable platform strategy: making our data, intelligence, and recommendations available across customer, partner, and AI workflows.
Define how transforms creative, media, brand, platform, and performance data into ranked, explainable, and actionable recommendations.
Build products for both human and machine consumers, including enterprise workflows, structured APIs, developer tools, and agentic systems.
Partner closely with engineering and data science on data models, recommendation logic, system architecture, evaluation, and technical tradeoffs across quality, latency, and cost.
Establish standards for grounding, confidence, explainability, provenance, actionability, and constraint adherence.
Design reinforcement learning loops to power continuous platform improvement.
Support integrations with media platforms, creative platforms, agencies, AI providers, and marketing infrastructure.
Use AI tools directly across research, requirements, prototyping, analysis, testing, documentation, and product operations.
Drive adoption of capabilities with customers and partners, not just delivery.