Product Manager Level 2 - Cincinnati
1 day ago
Job description
Location:
Cincinnati, OH
Salary:
$70.00 USD Hourly - $75.00 USD Hourly
Description:
The Product Manager drives end-to-end product strategy, execution, and lifecycle management. This includes defining product vision, deeply understanding user needs, prioritizing requirements, and ensuring the product delivers measurable business impact.
You'll work across engineering, design, data, and cross-functional partners to build solutions that are scalable, user-centric, and aligned with the company's long-term goals.
Success in this role requires balancing customer value, technical constraints, and business outcomes while maintaining a strong focus on quality, adoption, and user satisfaction.
Core SkillsMust-Have
Strong product strategy and prioritization abilities
Solid understanding of data platform fundamentals
Literacy in machine learning concepts
Clear, concise communication with technical and non-technical stakeholders
Ability to design experiences that serve expert users without excluding beginners
Excellent documentation, onboarding flow design, and workflow understanding (beyond just APIs)
Strong Differentiators
Knowledge of MLOps
Comfort with experimentation frameworks and metrics
Understanding of Responsible AI principles
Platform-centric UX thinking
Stakeholder Management
Align engineering, data science, legal/compliance, operations, and business teams
Translate technical limitations into business-relevant tradeoffs
Set realistic expectations regarding ML uncertainty, iteration, and performance variability
Data Foundations
Familiarity with structured, semi-structured, and unstructured data
Batch vs. streaming pipelines
Data quality principles (accuracy, completeness, timeliness)
Observability, lineage, metadata, schema management, and versioning
Platform Thinking
APIs, SDKs, and self-service tools
Multi-tenant vs. single-tenant architectures
Scalability, performance, and cost tradeoffs
Internal vs. customer-facing platform considerations
ML Fundamentals for PMs
Supervised vs. unsupervised models
Training vs. inference
Features, labels, and datasets
Key evaluation metrics (precision, recall, AUC, RMSE, etc.)
Understanding overfitting and generalization
Realities of ML Products
Outputs are probabilistic
Performance degrades over time (data drift, concept drift)
Data quality often matters more than algorithms
Development is iterative and experimental
Areas to Understand Deeply
Model training pipelines
Deployment patterns (batch, real-time, edge)
Monitoring, retraining, and versioning
Rollbacks and experimentation (A/B, canary releases)
Metrics & Experimentation
Metrics to Balance
Business:
revenue, conversion, cost savings
Model:
accuracy, recall/precision
Data:
freshness, null rates, coverage
Platform:
latency, uptime, adoption
Experimentation Skills
Designing tests where outcomes aren't binary
Interpreting noisy or delayed signals
Knowing when metrics are misleading
Key Responsibilities
Manage all technical aspects of the product throughout its lifecycle
Collaborate with stakeholders, vendors, and partners to ensure successful delivery
Maintain and communicate product catalog and roadmap
Define and monitor key product metrics to elevate customer experience
Gather, refine, and analyze requirements to ensure they're testable and traceable
Define MVP criteria and accelerate release cycles
Drive backlog prioritization and requirement refinement using agile tools
Facilitate walkthroughs, demos, workshops, and sprint ceremonies
Estimate work efforts using PI and sprint planning
Identify dependencies, risks, and cross-team impacts
Break complex product visions into actionable initiatives and features
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