Designing Clarity and Connection in Complex Science
[This engagement is ongoing and protected under NDA. Details will be removed where necessary.]
In partnership with a global pharmaceutical company, our consulting team embarked on a multi-year digital transformation of the R&D organization. This program is designed to modernize how scientific work, data, and decisions connect across the enterprise. The initiative integrates a new data platform, domain-specific data products, and AI-driven solutions to help scientists move from manual, fragmented processes to connected, insight-rich workflows.
The pharmaceutical R&D organization I am partnered with is rich in scientific talent but constrained by fragmented systems. Nearly 70% of teams reported difficulty reusing work, and most critical data lived in spreadsheets, homegrown tools, or memory. Scientists want to focus on the science, but instead spend hours copying, pasting, and reconciling data.
As the sole designer and researcher on a 50-person digital transformation initiative, I became the translator between scientists, data engineers, and leadership, helping shape both the vision and execution of a $XX Million, three-year transformation. My goal: design human-centered systems that make structured, governed data feel as intuitive as a lab notebook, yet powerful enough to fuel AI-driven discovery.
Discovery has involved 1:1 interviews, lab tours, service blueprints, and concept mapping across analytical methods and bioprocessing teams thus far. I have built deep domain fluency, creating a repository of lab process videos, research papers, and SME notes that allowed me to bridge the language divide between science and software. (API means something very different in pharma discovery than it does in the technology realm)
By making data findable, accessible, interoperable, and reusable (FAIR), we could unlock faster insights, fewer delays, and greater confidence across R&D, moving scientists from reactive problem-solving to proactive exploration.
Designing for Trust, Insight, and Scale
One of my first contributions was redesigning the Work Request Portal, which will replace an error-prone Excel form used to request and track assays. The new interface segments tasks, embedded complex logic to reduce errors, and automatically captures metadata for downstream data products. Scientists immediately recognized the value: one process development scientist noted it would “make scientists cry with joy.”
Beyond features, I have created foundational systems that scale design impact across three delivery pods. This includes a shared component library, reusable discovery frameworks, and a UX pattern set for AI-driven experiences that emphasize transparency and human control. These tools bring consistency and speed to teams building data products and interfaces across the organization.
This work also helped secure executive trust. I partnered with leadership from the earliest strategy phase: facilitating hybrid discovery sessions with over 60 participants, as well as co-presenting value-stream maps and design findings that helped win commitment for the 3-year contract. Later, I presented my UX work on the Work Request Portal during an executive showcase, which was so well received that I was asked to record a demo for company-wide distribution.
Through this work, I have learned that deeply technical, data-driven environments need design more than ever. FAIR data pipelines and AI models only reach their potential when paired with interfaces that feel clear, credible, and human. My ongoing focus is creating experiences where scientists can connect with data as intuitively as they form hypotheses—bridging the gap between human curiosity and machine intelligence to drive pharmaceutical discovery forward.
One of my first contributions was redesigning the Work Request Portal, which will replace an error-prone Excel form used to request and track assays. The new interface segments tasks, embedded complex logic to reduce errors, and automatically captures metadata for downstream data products. Scientists immediately recognized the value: one process development scientist noted it would “make scientists cry with joy.”
Beyond features, I have created foundational systems that scale design impact across three delivery pods. This includes a shared component library, reusable discovery frameworks, and a UX pattern set for AI-driven experiences that emphasize transparency and human control. These tools bring consistency and speed to teams building data products and interfaces across the organization.
This work also helped secure executive trust. I partnered with leadership from the earliest strategy phase: facilitating hybrid discovery sessions with over 60 participants, as well as co-presenting value-stream maps and design findings that helped win commitment for the 3-year contract. Later, I presented my UX work on the Work Request Portal during an executive showcase, which was so well received that I was asked to record a demo for company-wide distribution.
Through this work, I have learned that deeply technical, data-driven environments need design more than ever. FAIR data pipelines and AI models only reach their potential when paired with interfaces that feel clear, credible, and human. My ongoing focus is creating experiences where scientists can connect with data as intuitively as they form hypotheses—bridging the gap between human curiosity and machine intelligence to drive pharmaceutical discovery forward.
One of my first contributions was redesigning the Work Request Portal, which will replace an error-prone Excel form used to request and track assays. The new interface segments tasks, embedded complex logic to reduce errors, and automatically captures metadata for downstream data products. Scientists immediately recognized the value: one process development scientist noted it would “make scientists cry with joy.”
Beyond features, I have created foundational systems that scale design impact across three delivery pods. This includes a shared component library, reusable discovery frameworks, and a UX pattern set for AI-driven experiences that emphasize transparency and human control. These tools bring consistency and speed to teams building data products and interfaces across the organization.
This work also helped secure executive trust. I partnered with leadership from the earliest strategy phase: facilitating hybrid discovery sessions with over 60 participants, as well as co-presenting value-stream maps and design findings that helped win commitment for the 3-year contract. Later, I presented my UX work on the Work Request Portal during an executive showcase, which was so well received that I was asked to record a demo for company-wide distribution.
Through this work, I have learned that deeply technical, data-driven environments need design more than ever. FAIR data pipelines and AI models only reach their potential when paired with interfaces that feel clear, credible, and human. My ongoing focus is creating experiences where scientists can connect with data as intuitively as they form hypotheses—bridging the gap between human curiosity and machine intelligence to drive pharmaceutical discovery forward.
