Product Manager Level 2 - Cincinnati
2 days ago
Job description
The Product Manager is responsible for the product planning and execution throughout the Product Lifecycle, including gathering and prioritizing product and customer requirements, defining the product vision, and ensuring revenue and customer satisfaction goals are met.
The Product Manager's job also includes ensuring that the product supports the company's overall strategy and goals.This role supports an eCommerce fulfillment environment that manages pickup, third-party delivery (Instacart and DoorDash), and operations.
The team is building a platform focused on order submission, selection, and routing, with an emphasis on operational reporting, process optimization, and demand forecasting.
About the RoleThe Product Manager is responsible for the product planning and execution throughout the Product Lifecycle, including gathering and prioritizing product and customer requirements, defining the product vision, and ensuring revenue and customer satisfaction goals are met.
ResponsibilitiesManage all technical aspects of product through product lifecycle
Work directly and indirectly with business stakeholders, vendors and third parties to ensure execution of deliverables
Create, maintain and communicate product catalog and technology roadmaps, including near-term delivery, to engage stakeholders across the organization
Identify, measure and improve key product catalog metrics to enhance the customer experience, and create a compelling, relevant product vision using web metrics, customer insights, feedback, research and internal operational metrics
Elicit, define and analyze medium to complex requirements in various formats ensuring they are testable, measurable and traceable
Set criteria for minimum viable product to increase the speed/frequency with which enhancements and new capabilities are delivered
Lead the appropriate teams to refine, prioritize and manage requirements using various tools (e.g., templates, team backlogs, requirements management or agile task management applications)
Lead requirement walk-throughs with key stakeholders using various methods (e.g., team demos, workshops, sprint planning and backlog refinement sessions)
Identify and estimate anticipated work efforts based on priority using requirement work plans, program increment (PI) planning, and sprint planning
Define and resolve dependencies, issues and risks and identify impacted areas through team collaboration
Break down a medium to complex vision into smaller projects, initiatives or features
Qualifications
Skills:
Must-Have
Product strategy & prioritization
Data platform fundamentals
ML literacy
Stakeholder communication
Designing for expert users without alienating new ones
Clear documentation and onboarding flows
Understanding user workflows—not just APIs
Strong Differentiators
MLOps understanding
Experimentation and metrics fluency
Responsible AI leadership
Platform UX thinking
Stakeholder Management
Required Skills
Align business leaders, engineers, data scientists, legal/compliance, and ops
Translate technical constraints into business-relevant language
Manage expectations around ML uncertainty and iteration
Preferred Skills
Data Concepts You Should Be Fluent In
Data types:
structured, semi-structured, unstructured
Data pipelines (batch vs. streaming)
Data quality dimensions:
accuracy, completeness, timeliness
Data lineage and observability
Metadata, schemas, and versioning
Platform Thinking
APIs, SDKs, and self-service capabilities
Multi-tenant vs. single-tenant design
Performance, scalability, and cost tradeoffs
Internal vs. external (customer-facing) platforms
Machine Learning Fundamentals Every PM Should Know
Supervised vs. unsupervised learning
Training vs. inference
Features, labels, and training data
Model evaluation metrics (precision, recall, AUC, RMSE, etc.)
Overfitting vs. generalization
ML Product Realities
ML outputs are probabilistic, not deterministic
Model performance degrades over time (data drift, concept drift)
Improving models often requires better data, not better algorithms
ML development is experimental and iterative
Areas that must be understood
Model training pipelines
Model deployment patterns (batch, real-time, edge)
Model monitoring and retraining
Versioning of models and data
Rollbacks and experimentation (A/B tests, canary releases)
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