A/B Testing, Algorithms, Application Programming Interface (API), Artificial Intelligence (AI), Customer Satisfaction, Data Management, Data Modeling, Data Quality, Data Science, Establish Priorities, Experiment Design, Leadership, Legal, Literacy, Machine Learning, Metadata, Metrics, Performance Modeling, Product Lifecycle, Product Management, Product Planning, Product Strategy, Product Support, Requirements Management, User Interface/Experience (UI/UX)
Description
Job Title: Product Manager
Location: Cincinnati OH
Duration: 12+ month contract
Payrate: $75/hr
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.
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
- Align business leaders, engineers, data scientists, legal/compliance, and ops
- Translate technical constraints into business?relevant language
- Manage expectations around ML uncertainty and iteration
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)
Metrics You'll Need to Balance
- Business metrics (revenue, conversion, cost savings)
- Model metrics (accuracy, precision/recall)
- Data metrics (coverage, freshness, null rates)
- Platform metrics (latency, uptime, adoption)
Experimentation Skills
- Designing experiments when outcomes aren't binary
- Interpreting noisy or delayed signals
- Knowing when not to trust metrics blindly