FROM PIXEL TO PERFORMANCE — Y:2019
SARAH
ANDREW
I'm Dr. Sarah Andrew, a UX Research & Recursive Learning Strategist with over five years of experience architecting self-optimizing user experiences at the intersection of data-driven testing, embedded analytics, and agentic systems.
SCROLL
As an Agentic Frontend Designer and Product Owner with a strong foundation in Human-Computer Interaction and Computing Science, I focus on defining product requirements, prioritizing features via quantitative scoring models, and guiding cross-functional teams to deliver self-optimizing, scalable digital products.
My work involves leading product discovery through usability testing and behavioral analytics, synthesizing insights into rubric-scored product requirements, and informing prioritization and roadmap decisions through recursive learning cycles that target the lowest-performing UX sections first.
Through qualitative and quantitative research, sectionalized usability evaluations, and stakeholder collaboration, my goal is to deliver products that meet user needs and business yield targets — using self-scoring rubrics of my own design to ensure each control surface is continuously measured, scored, and optimized through embedded analytics and micro-agent orchestration.
Recursive Learning Systems Manager — Synthetic Data × Agentic UX Optimization
Chatlabs
2024 — Present
Managed product delivery of AI-enabled backend and mobile platforms, defining how synthetic data pipelines feed recursive learning cycles that drive sectionalized UX optimization across control surfaces.
Collaborated with engineering and design teams to define requirements and prioritize improvements by mapping user feedback signals to self-scoring rubric dimensions, ensuring each iteration cycle targets the lowest-performing UX sections first.
Conducted structured usability evaluations using rubric-aligned scoring criteria, synthesizing quantitative findings into prioritized product recommendations that close the loop between testing data and adaptive interface logic.
Authored product documentation and stakeholder communication frameworks that translate recursive learning outcomes — rubric scores, optimization cycle results, and section-level performance deltas — into business-legible delivery narratives.
UX Research & Testing Intern — Sectionalized Flow Optimization and Data-Driven Requirements
Idealoft Studios
2019 — 2020
Collaborated with stakeholders to translate business and user requirements into product features for fintech mobile applications, mapping each feature to measurable usability benchmarks tied to task-completion efficiency and navigation performance.
Supported definition of sectionalized user flows and functional requirements, structuring each flow segment as an independently testable control surface with defined success criteria for iterative evaluation.
Partnered with engineering and design teams to deliver responsive product experiences, contributing to cross-functional workflows where usability testing data informed each sprint's optimization priorities.
UX Control Surface Strategist — Recursive Analytics, Testing, and Rubric-Driven Optimization
Rochester Institute of Technology
2020 — 2024
Owned product initiatives for mobile and web platforms, defining product requirements by decomposing interfaces into sectionalized control surfaces — each with independent telemetry streams, rubric dimension mappings, and measurable optimization thresholds.
Partnered with engineers, designers, and researchers to prioritize features using self-scoring rubric baselines, evaluating each candidate feature against quantitative performance data per UX section to determine iteration priority.
Led discovery efforts including task-based usability research and moderated testing sessions, feeding structured findings directly into recursive learning workflows that informed roadmap decisions through scored evidence rather than assumption.
Translated research insights into product requirements, user stories, and acceptance criteria grounded in rubric-derived metrics — each story traceable to a specific section-level performance gap identified through the recursive evaluation cycle.
Developed scalable UX testing patterns and scoring guidelines to ensure rubric consistency and measurement repeatability across product surfaces.
Ph.D. Computing and Information Science
Rochester Institute of Technology
2021 — 2026
Built advanced expertise in data-driven UX optimization, recursive learning system design, and evidence-based decision-making for self-optimizing digital products. Developed the ability to translate research insights into rubric-scored product requirements, prioritization decisions, and scalable UX testing frameworks across mobile, web, and SaaS control surfaces.
B.C.A Bachelor of Computer Applications
Christ University
2016 — 2019
Gained a technical foundation in software systems, data structures, and application development, supporting effective collaboration with engineering teams and informed product decision-making.
M.S. Human-Computer Interaction (HCI)
Rochester Institute of Technology
2019 — 2021
Developed a strong foundation in product thinking, user research, usability testing, and interaction design, with a focus on informing product requirements, quantitative feature prioritization, and data-driven product delivery.
Authentication Challenges in Customer Service Settings Experienced by Deaf and Hard of Hearing People
2023
CHI
92%
Recursive Learning & Self-Scoring Rubric Design
90%
Sectionalized UX Optimization & Testing
88%
Agentic Workflow Coding (Claude API / Claude Code)
87%
Embedded Analytics & Telemetry Architecture
85%
Micro-Agent Orchestration & Research Synthesis
82%
Cross-Platform Control Surface Decomposition
Seeking Agentic Frontend Designer & Product Owner roles — Open to Relocate
Focused on self-optimizing digital products, recursive learning systems, and data-driven UX at scale.