Product Manager Level 2

• 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

• MLOps understanding
• Experimentation and metrics fluency
• Responsible AI leadership
• Platform UX thinking

• 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 types: structured, semi structured, unstructured
• Data pipelines (batch vs. streaming)
• Data quality dimensions: accuracy, completeness, timeliness
• Data lineage and observability
• Metadata, schemas, and versioning

• APIs, SDKs, and self service capabilities
• Multi tenant vs. single tenant design
• Performance, scalability, and cost tradeoffs
• Internal vs. external (customer facing) platforms

• 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

• 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)

• 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

• 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

deepak.kumar@leadstackinc.com