ZSers in Buenos Aires work to optimize clinical trials

When it comes to healthcare innovation, being fast, efficient and accurate is critical. At ZS, we know that optimizing clinical trials is essential to improving health outcomes for all—ensuring more patients gain access to the latest treatments and life-saving medicines. ZSers like Gunjar Ahuja work alongside our patient support and R&D teams to streamline trial processes and support a smoother treatment journey for patients.

Where passion changes lives

Based in Buenos Aires, Gunjar supports research and development efforts to enhance the clinical trial process and deliver better experiences for both sponsors and patients. Clinical trials can take six to 10 years and cost billions—delaying patient access to vital care. To reduce this timeline and ease the burden for sponsors and participants alike, Gunjar and his team focus on increasing efficiency at every stage.

“Clinical trials for oncology rely heavily on historical data,” Gunjar explains. “For each patient, we need to know what medications are appropriate, which symptoms to monitor and what sample size to use. By working with business analysts, we create semiautomated data pipelines that reduce months of manual work. This leads to faster, smarter solutions—and ultimately, better outcomes.”

Gunjar has helped clients reduce trial timelines by months, accelerating the delivery of new therapies. “Getting treatments to patients faster can have an immense impact on their health. That’s what drives me,” he says. “It’s incredibly meaningful to know that my work is improving lives.”

Large biopharma companies often run multiple trials in parallel, making data consolidation and organization a major challenge. But organizing this data is critical to uncover insights, streamline operations and enable faster, more frequent trials. With the exponential growth of life sciences data, the ability to harness and interpret it effectively has never been more important—and that’s where ZS brings value.

“Some companies run hundreds of trials at once,” Gunjar says. “Each might use different attributes or data collection methods, which makes it tough to analyze consistently. That’s where our work becomes essential.”

To solve this, ZS developed a machine learning technique that standardizes disparate clinical trial data into a single, unified model. This transformation turns raw source data into structured datasets that can be searched, sorted and analyzed quickly.

“With this approach, we can reduce data preparation time from months to hours,” Gunjar explains. “It enables researchers to move faster and focus more on discovery.”

Across ZS, teams of experts collaborate to make small, scalable changes with big impact. Thanks to Gunjar’s work, patients get access to innovative treatments faster—helping ZS deliver on its mission to improve health outcomes for all.

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