Enter a job title or keyword

Product Manager Level 2

LeadStack Inc.

Job Location:

Blue Ash, OH - USA

Hourly Salary: USD 70 - 80

Posted: 29 September 2026 (21 hours ago)

Application Deadline: 27 December 2026

Vacancies: 1 Vacancy

Job Summary

LeadStack Inc. is an award-winning one of the nations 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 were 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 Managers job also includes ensuring that the product supports the companys overall strategy and goals.

Skills:
MustHave

Product strategy & prioritization
Data platform fundamentals
ML literacy
Stakeholder communication
Designing forexpertusers without alienating new ones
Clear documentation and onboarding flows
Understanding user workflowsnot 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 intobusinessrelevant language
Manage expectations around ML uncertainty and iteration

Data Concepts You Should Be Fluent In

Data types: structured semistructured 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 selfservice capabilities
Multitenant vs. singletenant design
Performance scalability and cost tradeoffs
Internal vs. external (customerfacing) 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 areprobabilistic not deterministic
Model performance degrades over time (data drift concept drift)
Improving models often requiresbetter data not better algorithms
ML development is experimental and iterative

Areas that must be understood

Model training pipelines
Model deployment patterns (batch realtime edge)
Model monitoring and retraining
Versioning of models and data
Rollbacks and experimentation (A/B tests canary releases)

Metrics Youll 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 arent binary
Interpreting noisy or delayed signals
Knowing whennotto 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