What are the responsibilities and job description for the Product Manager ( Research ) position at Talener?
Role: Product Manager (Research)
Location: New York, NY (Hybrid)
Compensation: $140,000 - $185,000 Comprehensive Benefits
About The Opportunity
We are recruiting for a high-growth, core innovation team within a global digital media and enterprise intelligence ecosystem. The team is building a proprietary discovery platform that transforms high-volume streams of real-world information into structured data assets, predictive indicators, and machine-readable context layers.
This technology directly feeds next-generation enterprise applications, AI models, and automated analytical workflows. In this role, you will lead early-stage exploratory research, quantitative experimentation, and technical concept validation to shape the future of our data architecture.
Requirements & Qualifications
Location: New York, NY (Hybrid)
Compensation: $140,000 - $185,000 Comprehensive Benefits
About The Opportunity
We are recruiting for a high-growth, core innovation team within a global digital media and enterprise intelligence ecosystem. The team is building a proprietary discovery platform that transforms high-volume streams of real-world information into structured data assets, predictive indicators, and machine-readable context layers.
This technology directly feeds next-generation enterprise applications, AI models, and automated analytical workflows. In this role, you will lead early-stage exploratory research, quantitative experimentation, and technical concept validation to shape the future of our data architecture.
Requirements & Qualifications
- 5 years of professional experience driving technical product discovery across data platforms, machine learning products, automated retrieval systems, or complex analytics platforms.
- Demonstrated ability to take ambiguous data challenges and turn them into structured research plans and actionable development roadmaps.
- Practical, hands-on fluency interrogating complex datasets using tools like SQL, Python, and Jupyter environments.
- Operational understanding of modern data technologies, including retrieval-augmented generation, vector embeddings, structured schemas, ontologies, and model performance metrics.
- Exceptional communication skills with a track record of distilling technical insights for cross-functional teams, executive leadership, and commercial clients.
- Strong commercial instinct to balance experimental curiosity with scalable business outcomes.