Project Description
At an Institute, I led the development of a portfolio of digital applications by translating behavioral science insights into actionable product strategies. Operating at the intersection of product management, Neuromarketing, UX, and research, I collaborated closely with neuroscientists, designers, and engineers to integrate data from eye-tracking, online studies, and EEG into the product development lifecycle.
In addition to my product leadership responsibilities, I was also actively involved in frontend development, building web applications used in online behavioral studies and experimental setups. This included implementing user interfaces, ensuring data capture accuracy, and optimizing performance across devices—critical for maintaining the integrity and reliability of research results.
My role focused on bridging the gap between complex scientific findings and practical product decisions. I facilitated design sprints, defined product requirements, and translated cognitive insights into user journeys, interaction patterns, and interface design. By combining experimental data with real-world usage analytics, I helped create digital experiences that are not only functional, but aligned with how users actually perceive and interact with information.
In parallel, I contributed to the scalability of these applications by supporting DevOps strategies and improving deployment processes, enabling faster iteration and continuous validation of product hypotheses.
Problem solved:
The project addressed a key gap in digital product development: the disconnect between how users report their behavior and how they actually behave. Traditional UX approaches often rely on self-reported feedback or limited testing environments, which can lead to incomplete or biased insights.
A central challenge was making complex neuroscience data usable within fast-paced product teams. This required translating signals such as attention patterns, cognitive load, and emotional response into concrete design and product decisions. At the same time, building reliable web-based study environments ensured that high-quality behavioral data could be collected at scale, forming a solid foundation for data-driven product improvements.
By combining robust data collection with evidence-based design, we shifted from assumption-driven decisions to a more precise and measurable approach to UX optimization.
Technologies used:
- Figma for UX/UI design and prototyping
- Azure DevOps for deployment and automation
- Angular for Frontend Development
- Eye-tracking & EEG data tools
- Agile / Design Sprint methodologies
- Responsive Web Development
- Analytics tools for behavioral tracking
Outcome:
By aligning product decisions with real cognitive and behavioral data, the digital applications achieved a 35% reduction in bounce rate, reflecting stronger user engagement and more intuitive user experiences. Improvements in information hierarchy, interaction design, and visual focus contributed directly to these results.
The web applications developed for online studies enabled scalable and reliable data collection, supporting continuous insight generation and faster validation of product hypotheses. In parallel, DevOps automation reduced deployment time by 40%, allowing teams to iterate more quickly and bring improvements to production faster.
Overall, the project highlights a unique combination of product leadership, technical execution, and scientific rigor, demonstrating how deeply integrated data, UX, and engineering can drive meaningful impact in digital product development.
