Data Scientist Sapient is a fast-growing early-stage biotech company headquartered in San Diego, CA. Our focus is on delivering multi-omics services to accelerate drug development and transform personalized medicine. 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. We are a highly collaborative team of passionate scientists, clinicians, and engineers with expertise in various disciplines, committed to making innovations with scientific rigor. Ultimately, our platform technology enhances the precision and efficacy of treatments, touching and enhancing countless lives. About The Role: 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 a senior data scientist. This role will focus on solving structured problems and deriving insights from metabolomics, proteomics, genomics, 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 engineers, chemists, biologists, clinicians 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. Responsibilities: Work with supervision and guidance to analyze datasets, mine databases, and create code according to Company-defined good practice, to drive optimization and improvement within and across projects.Apply statistical and machine learning methods with rigor to high-dimensional omics and clinical data to derive 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.Experience and Qualifications: MS, or 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.Experience in clinical or epidemiological study data analysis is preferred.Consistent use of git or other code management tools is preferred.Proficiency in Python or R for product-level code development.Strong use of clean code principles, testing principles, and understanding of CI/CD processes is preferred.Experience with database query language (e.g. SQL) and cloud computing (e.g. AWS) is preferredExcellence 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 Location – San Diego, California (Sorrento Valley) Position Type – Full-time, on-site Compensation Range – $100K-$135K 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.
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