Data Scientist III

Sapient is an early-stage fee for service, multi-omics contract research organization in San Diego, CA. Our focus is on accelerating drug development and transforming personalized medicine for our pharma and biotech clients. We are an agile, highly collaborative team of passionate scientists, committed to making innovations with scientific rigor, and executing in a timely manner in accordance with key regulatory standards.

Combining state-of-the-art mass spectrometry technology with advanced biocomputational services and multifaceted human health data, we enable biomarker discovery at unprecedented speed and scale to provide a deeper understanding of human biology and effectively align biological targets, patients, and drug therapies in support of our clients.

We are in search of a passionate Data Scientist to support Sapient’s biomarker discovery service offerings and R&D effort with insights gained from analyzing clinical and multi-omics data.

This role is within the data science team and working under the oversight and mentorship of Head of Data Science. This role will focus on solving structured biological/clinical problems and deriving insights from proteomics, metabolomics, and clinical data. The data scientist will have experiences using a variety of data mining and statistical analysis methods and platforms, building and implementing models, using and creating algorithms, and contributing to final insights on biomarker discoveries.

The data scientist will work with a wide range of stakeholders and collaborate with chemists, biologists, clinicians, business associates within and outside of the Company, and will have a demonstrated ability to drive biomarker discovery with their data-based insights. This is a great opportunity to tackle a diverse set of problems while shaping the future of drug development and healthcare.

• Work with supervision and guidance to analyze datasets, mine databases, and create code according to Company-defined good practice, to drive rigor, efficiency, and reproducibility within and across projects.
• Apply statistical and machine learning methods with rigor to high-dimensional omics and clinical data, and think critically about biological/clinical questions to derive grounded and impactful insights.
• Collaborate with scientists and clinicians internally and externally to investigate scientific and medical problems with rigorous analyses and critical thinking.
• Communicate methods used, data insights, and scientific findings to collaborators and clients with clarity.

• PhD in Biostatistics, Bioinformatics, Computational Biology, Bioengineering, Data Science, Applied Math, Physics, or Life Science related scientific field.
• 2+ years of hands-on experience in statistical analysis and data mining, as well as modeling and coding.
• Deep knowledge and understanding of statistical methods and basic machine learning algorithms.
• Life science domain knowledge and good intuition in applying appropriate data analysis approaches to best address questions given the scientific context.
• Consistent use of git or other code management tools.
• Proficiency in Python for product-level code development.
• Strong use of clean code principles, testing principles, and understanding of CI/CD processes.
• Experience with prompt engineering and major coding and scientific AI tools.
• Experience with database query language (e.g. SQL) and cloud computing (e.g. AWS) is preferred.
• Experience in clinical or epidemiological study data analysis is preferred.
• Excellence in communicating findings by visualizing the results, composing presentation slides, and forming a scientific report.
• Self-motivated, dedicated, and accountable.
• Curious and eager to grow, thriving in a dynamic and intellectually challenging environment.
• Great team player.

Position Type – Full-time, on-site

Compensation Range – $130K-$150K

Sapient is an equal opportunity employer. We value diversity, inclusivity and belonging of all people from different cultures socioeconomic backgrounds. We do not discriminate based on race, ethnicity, gender, age, sex, disability or any other secondary biases