We’re partnering with an innovative life sciences tools company developing a next-generation proteomics platform that’s reshaping how large-scale protein data is analyzed and applied. Their technology supports everything from drug discovery to AI-enabled target identification.
This Senior Bioinformatics Scientist (Applications & ML) role bridges bioinformatics development and customer collaboration . You’ll work hands-on with proteomics datasets to design scalable pipelines, interpret results, and translate complex analyses into actionable insights that scientists can use.
The ideal candidate combines technical depth in data analysis and machine learning with the ability to communicate effectively across scientific, product, and customer teams.
Key Responsibilities
- Build, refine, and document bioinformatics and ML pipelines for proteomic and multi-omic data analysis.
- Collaborate with internal teams (software, data, and product) to implement analysis workflows into production systems and customer-facing tools.
- Engage directly with collaborators or customer partners to understand scientific goals and deliver interpretable, decision-driving results.
- Identify and frame the “right story” in complex data, ensuring analyses focus on biological meaning and customer value.
- Contribute to the broader strategy of the bioinformatics applications layer, helping shape what capabilities should be built next.
- Communicate findings clearly through technical reports, presentations, and publications.
- PhD or MSc in Bioinformatics, Computational Biology, or related field.
- 4–8 years’ experience in bioinformatics, data science, or computational biology (life sciences tools, biotech, or pharma).
- Proficiency in Python or R and familiarity with ML libraries (scikit-learn, PyTorch, TensorFlow).
- Experience with proteomics or transcriptomics datasets; comfort integrating multiple omic data types.
- Excellent written and verbal communication skills; capable of discussing results with both scientists and business stakeholders.
- Prior exposure to customer-facing or cross-functional collaboration (e.g., pre-sales, applications, or product support) is highly advantageous.
- Comfortable in a hands-on, build-from-the-ground-up environment.
- Hands-on technical ability paired with strong scientific storytelling.
- Awareness of how analytical work connects to customer impact.
- Comfort toggling between independent coding and collaborative problem-solving.
- A builder’s mindset — energized by early-stage challenges and tangible outcomes.
