Product Manager Level 2 - Cincinnati, OH
18 hours 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 Skills
Must-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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Contact:
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