Product Manager Level 2

Lead Stack Inc. is an award-winning, one of the nation's fastest-growing, certified minority-owned (MBE) staffing services provider of contingent workforce. As a recognized industry leader in contingent workforce solutions and Certified as a Great Place to Work, we're proud to partner with some of the most admired Fortune 500 brands in the world.

Job Title:
Product Manager Level 2
Location:
Blue Ash, OH, 45241
Job Duration: 6 Months

Pay rate: $70/hr - $80/hr

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.

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

Key Responsibilities

• Manage 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

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