The Challenge
A large, global pharmaceutical company working on new cancer treatments was running high-throughput drug screens. These experiments generated thousands of microscopy images, and analyzing them by hand was slow and often inconsistent. The team was struggling to keep up, which delayed their ability to identify promising compounds for follow-up.
They knew they needed someone with image analysis expertise but weren’t ready to bring on a full-time hire. Instead, they reached out to us to help find someone who could step in and get the work moving in the right direction.
Our Approach
We started by speaking with the company’s scientists to understand their goals – not just technically, but also how the new hire would need to work within the existing team. They needed someone who could handle large-scale imaging data and develop a more automated analysis pipeline. But they also wanted someone who could think independently, communicate clearly with non-technical collaborators, and troubleshoot problems under time pressure.
After evaluating several candidates, we introduced a data scientist with strong experience in image analysis and computational biology. The candidate had a solid background in Python, CellProfiler, and machine learning. Just as importantly, they had experience working closely with life science teams and navigating projects where requirements evolved as new data came in. Our Client loved them, and they got started within weeks.
Once embedded with the team, the data scientist began by reviewing the experimental setup and the kinds of phenotypes the researchers were trying to capture. They worked closely with lab scientists to understand what the images represented and what kinds of variation were most relevant.
They then built a pipeline to automate cell segmentation, extract morphological features, and classify different cell states. Given the volume of data, they used workflow management tools (Nextflow) and cloud computing (AWS) to parallelize the processing and avoid bottlenecks.
To go a step further, they trained machine learning models to identify subtle changes in cell behavior that might indicate a drug response – changes that might be missed using simpler rule-based methods.
Finally, they developed an R Shiny dashboard so that biologists on the team could explore the results interactively. The dashboard allowed users to filter data, view images by classification outcome, and generate visual summaries without needing to write code.
The Outcome
The new pipeline reduced the time it took to analyze image data by about 90%. That meant researchers could complete screens much faster and prioritize hits more efficiently. The classification models improved accuracy, cutting down on false positives and helping the team focus on compounds with real potential.
The visualization tools made the results easier to interpret and discuss across the team, which improved collaboration and helped researchers make decisions based on actual data trends rather than intuition or spot-checking.
This wasn’t just about hiring someone who could code. The key was finding a person who could communicate well, adapt to the research context, and solve problems independently, all of which are essential in fast-moving bioinformatics environments. By focusing on these traits, not just technical qualifications, we were able to help the company make real progress without the delay of onboarding a full-time hire.
Let’s talk
Don’t let staffing challenges hinder your progress. Partner with us to access the talent you need, when you need it. Contact us today to discuss your specific requirements and discover how our flexible staffing solutions can help you achieve your goals.
Originally published by Bridge Informatics. Reuse with attribution only.
