Staff product leader shaping strategy and scaling data and platform products.
I turn ambiguous, cross-functional problems into product strategy, durable platforms, and measurable business outcomes. My work spans 5G at Verizon, global payments at Samsung, and machine learning and analytics at Cargill.
I lead through influence: setting direction, aligning technical and business leaders, making tradeoffs explicit, and building operating clarity across teams. AI is one important part of that toolkit, grounded in the same discipline around data, reliability, economics, and trust.
Decisions, systems, and verified outcomes.
Turning a fragmented 5G platform roadmap into a data-driven product strategy
How I used customer behavior, system signals, and business impact to prioritize platform investments across competing teams.
From 24 hours to under 1 minute
Reimagining transaction processing and settlement for a global payments platform.
Can an AI agent be trusted to do the job?
Designing an evaluation framework for reliable multi-step AI workflows.
Using ML to improve demand forecasting
Statistical modeling and experimentation to support operational decisions.
Turn broad company goals and ambiguous customer problems into a clear product thesis, sequenced bets, and measurable outcomes.
Create alignment across engineering, design, architecture, operations, and business leaders without relying on reporting lines.
Prioritize shared capabilities that improve reliability, unlock multiple teams, and compound value across the organization.
Use customer behavior, experimentation, and operational signals to resolve debate, manage risk, and move the roadmap.
Frame the decision. Align the system. Commit with clarity. Learn in public.
A concrete leadership system for turning cross-functional ambiguity into an outcome roadmap and a faster learning loop.
See how I operate →Most escalations are the visible end of a mechanism nobody owns. I trace the mechanism first, then decide what to build.
When every team's request is urgent, behavior and failure data are the only neutral arbiter I've found that actually moves a roadmap.
Users forgive a missing capability far more readily than an unpredictable one. Predictability is what earns adoption.
Teams move faster when the decision, evidence, constraints, and accepted costs are visible to everyone.
One platform investment that unblocks four squads beats four features that each serve one.
When to use an LLM, and when deterministic code is the better product.
The framework I run before engineering starts: what good means, what wrong costs, what the latency and token budgets are, and what happens when confidence drops.
Read the frameworkSet a p95 budget per step, cut anything that cannot justify its share of the tail.
Retries and failed runs count. Route easy cases to smaller models.
Rank before packing. More context is not more accuracy.
Autonomous, assisted, or escalated, decided by confidence band.
Agent Evaluation Dashboard
A working 0 to 1 evaluation workbench for multi-step agent reliability. Edit candidate outputs, run deterministic checks, inspect expected responses, and download the synthetic dataset.
Open the labProduct vision · Portfolio choices · Roadmap sequencing · Build vs. buy · Outcome definition
APIs · Shared capabilities · Reliability · Scalability · Developer and operator workflows
Behavioral analytics · Experimentation · Forecasting · Operational signals · Decision systems
LLMs · Agentic workflows · Evaluation · Cost and latency · Risk · Human oversight
“Seher stands out as a product leader with a rare combination of strategic vision, meticulous attention to detail, and a deep empathy for user needs.”
“She always kept a can-do attitude and, in some cases, taught herself the necessary skills to do what was needed.”