Senior Product Manager
New
D
DscoutUX research
Location Flexibility: Dscout is proud to support a remote-first workforce and enable employees to work from almost anywhere. At this time, however, we are unable to hire in the following locations: Montana, Hawaii, Alaska, and Washington DC.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5–8 years of product management experience
- Required Skills
- Product ManagementData modeling
Requirements
- Have 5–8 years of product management experience, including owning a roadmap end-to-end from strategy through shipped outcomes.
- Have experience owning data-driven or ML-powered products, such as trust and safety, fraud, risk, recommendations, or scoring systems, and their evaluation decisions.
- Understand evaluation methods, including defining ground truth, choosing metrics, handling imperfect labels, and tuning thresholds.
- Have experience with data modeling and architecture, including event-driven systems, signal provenance, and structured and unstructured data.
- Use sound judgment on privacy and responsible data use, including fairness and bias in systems that score people.
- Be able to prototype with LLMs and understand their strengths and limitations.
- Have experience partnering with internal teams as customers and with platform or API consumers.
- Have experience in two-sided marketplaces or networks, ideally with a participant or supply-side focus.
- Be able to align senior stakeholders, make decisions with incomplete information, and coach other PMs.
- Have day-to-day fluency with AI tools such as Claude, Codex, Cursor, or similar, including personal agents or automated workflows.
Responsibilities
- Build and lead the roadmap and strategy for the Scout profile, from problem discovery through delivery and iteration.
- Define, ship, and evolve Scout quality, activity, and overall scores.
- Lead the rearchitecture of how Scout data is stored, updated, and used.
- Make the Scout profile central to matching Scouts to studies, partnering with recruiting and matching teams.
- Build ground-truth sets, measure precision and recall, set thresholds, and establish rollback plans for system changes.
- Partner with Engineering, Data, and Trust and Safety to detect and reduce fraud and low-quality participation.
- Specify and prototype evaluation tools for Research Operations and Support.
- Use LLMs to extract signals from open-ended responses, prototype ideas, and shape system behavior through prompting and context design.
- Track signal accuracy, false-positive and false-negative rates, drift, study quality, and Scout experience.
- Partner with Design and Research on usable and understandable tools and experiences.
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