Daniel P. Saba
At the intersection of human psychology, intelligent systems, and enterprise strategy.
I translate complex human behavioral patterns into actionable digital strategy, system optimization, and change enablement. By combining rigorous experimental design, multi-rater data cleaning, and mixed-methods research, I help organizations de-risk AI adoption, build user trust, and optimize human-AI workflows.
I am a Mixed-Methods Behavioral Researcher specializing in how humans interact with, adopt, and trust automated systems. My background spans four years of corporate operations at a Fortune 20 healthcare company (Elevance Health) and advanced graduate training in psychological statistics, experimental design, and machine learning evaluation workflows.
My work sits at the intersection of human behavior and intelligent systems, whether that means diagnosing how customers adopt and build trust in a product or how employees integrate generative AI into their daily workflows. Using mixed-methods research architectures grounded in behavioral science, I surface the psychological barriers and adoption gaps that standard usability testing and leadership intuition may overlook. The output is the same in either context: structured behavioral evidence that translates into validated design recommendations, deployment strategies, and training models that protect technical investment and accelerate human-technology integration.
Human-Centered AI Frameworks
Designers of conversational agents and social robots often build features for short-term engagement that inadvertently trigger user dependency, algorithmic deception, or system adoption friction.
Co-led a Qualitative Evidence Synthesis evaluating 96 design guidelines across 18 core human-computer interaction vectors. Engineered a dual-axis evaluative framework to isolate design traits that support real-world human connection versus those introducing relational risk. Managed data hygiene and rater calibration by calculating Fleiss' Kappa (κ = 0.917; κ = 0.896) and pairwise Cohen's Kappa, achieving over 90% exact agreement across the evaluation team.
Quantitative Behavioral Analytics
Digital environments and generative tools often induce user anxiety, cognitive fatigue, and negative self-evaluation, resulting in platform friction and user churn.
Designed and executed a multi-arm randomized controlled experiment (N=60) analyzing user behavioral output under varied cognitive conditions. Built a customized qualitative coding matrix to process unstructured user text data, extracting explicit linguistic variables (self-referents, descriptive terms, and emotional states). Conducted inferential modeling to isolate a substantial behavioral intervention effect size (d = .90) directly linked to predictive baseline traits (r = .48).
Human-AI Trust and Feedback Optimization
Deploying AI-driven feedback loops to train professionals requires systems that generate assessments users find credible, motivating, and behaviorally accurate.
Defined comprehensive construct validity frameworks to identify and track behavioral markers (active listening, empathetic tone, communication pacing) for computational modeling. Synthesized simulation-based learning and human-factors literature to optimize user flow, specifically accounting for psychological boundary variables including cognitive load and emotional arousal.
Human-Technology Interaction Researcher
- Design experimental protocols, construct validity frameworks, and qualitative coding schemas to optimize human-AI system alignment and validation.
- Utilize statistical frameworks (Fleiss'/Cohen's Kappa, regression modeling) to ensure data quality and clean unstructured linguistic data for machine learning evaluation workflows.
Administrative Operations & Crisis Specialist
- Manage high-stakes stakeholder interactions, active listening protocols, and swift behavioral de-escalation for individuals experiencing profound psychological distress.
- Collaborate cross-functionally with senior leadership, mental health professionals, and emergency resources to execute safety plans under tight timelines.
Strategic Sourcing Research Analyst
- Extracted, cleaned, and synthesized complex, high-volume datasets across enterprise systems (Domo, Ariba, ChromeRiver) under tight operational deadlines.
- Conducted comparative analysis and predictive hypothesis testing regarding supplier performance, cost-efficiency, and operational risk mitigation.
- Designed interactive data visualization dashboards and presented annual strategic reports directly to cross-functional senior executives to drive procurement decisions.
- Mixed-Methods Research Design
- Qualitative Evidence Synthesis (QES)
- Experimental & Quasi-Experimental Architecture
- Construct Validity Frameworks
- Survey Engineering
- Inter-Rater Reliability (Fleiss' κ, Cohen's κ)
- Linear & Quadratic Regression
- Sampling & Significance Testing
- Linguistic & Qualitative Data Coding
- SPSS, JASP, Stata
- Domo
- Qualtrics
- Advanced Excel
- Python, R
- English (Native)
- Spanish (Native)