Senior Product Manager, Data Products
New
D
DutchieCannabis Technology
Location: RemoteFull-TimeSenior
Salary149,000 - 201,000 USD per year
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Job Details
- Experience
- 7+ years of progressive experience in product management
- Required Skills
- Artificial IntelligenceMachine LearningProduct ManagementData engineeringNLP
Requirements
- 7+ years of progressive experience in product management, with demonstrated ownership of working with data products and production-grade machine learning systems.
- Experience turning internal data infrastructure into externally-facing products or platform capabilities.
- Strong technical fluency across data pipelines, ML/NLP systems, and data quality tooling.
- Experience with product catalog, taxonomy, or product information management (PIM) systems.
- Proven ability to manage complex cross-functional dependencies across multiple engineering teams.
- B2B software experience with an understanding of how platform products are adopted and configured by business operators.
- Experience leveraging AI/ML technologies (including LLMs) to improve product quality, automate classification, or drive intelligent matching.
- Excellent communication skills.
- Experience designing governance models for shared data assets.
- Comfort owning product identifier systems as a product problem.
Responsibilities
- Own the end-to-end product vision, strategy, and roadmap for a portfolio of data products consumed across ecommerce, POS, B2B, and analytics.
- Partner closely with Data Engineering to evolve the underlying data platform—including matching, classification, canonical naming, and quality—to deliver measurable customer and business outcomes.
- Define and ship data-driven experiences for our customer bases: retailer-facing, business-facing, and internal.
- Develop next-generation AI and Machine Learning systems, such as next best action and recommendations engines, to drive significant product improvements.
- Own the architecture and governance models for critical data assets, resolving conflicts and balancing canonical authority with contributor flexibility.
- Translate data coverage, quality, and structure into measurable business outcomes, such as merchandising conversion, receiving efficiency, or analytic integrity.
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