Job Details

We are seeking a highly skilled and innovative Senior Data Scientist to join our Data Science research and development team. Your work will contribute to improving patient care, optimizing clinical products, and advancing medical research. This position offers an exciting opportunity to leverage the power of artificial intelligence on one of the largest clinicogenomics real-world datasets in human history.

As a Senior Data Scientist, you will play a crucial role in designing and developing AI-enabled genomics algorithms. The ideal candidate will possess strong skills in data science, expertise in genomics machine learning, and experience with real-world clinical data.

What You Will Do:

1. Analyze large multimodal datasets to develop new AI-powered clinical reports, like IPS and TO.
2. Develop and characterize novel algorithms for predicting cancer subtype, patient outcome, and treatment response.
3. Collaborate with product, science, engineering, and business development teams to build the most advanced data platform in precision medicine.
4. Interrogate analytical results for robustness, generalization, and clinical impact.
5. Document, summarize, and present your findings to a group of peers and stakeholders.

Required Qualifications:

1. MS/PhD degree in a quantitative discipline (e.g. statistical genetics, cancer genetics, machine learning, bioinformatics, statistics, computational biology, biomedical informatics, or similar).
2. Experience working with genomic (e.g., DNA-seq, RNA-seq) or clinical (survival data, trials, real world evidence, claims) data.
3. Outstanding data analysis skills, with a particular focus on detailed characterization of genomics and clinical datasets for powering machine learning algorithms.
4. Experience with supervised and unsupervised machine learning algorithms used in genomics and clinical research: regression, classification, survival modeling, Kaplan-Meier, Cox regression.
5. Strong programming skills and experience with the python clinical+molecular data science stack: pandas, scikit-learn, lifelines, and jupyter.
6. Strong database and SQL skills: BigQuery, dbt.
7. Experience with engineering best practices for research computing (docker, git, code review, workflow managers, linux, cloud computing).
8. Thrive in a fast-paced environment and able to shift priorities seamlessly.
9. Experience with communicating insights and presenting concepts to diverse audiences.
10. Team player mindset and ability to work in an interdisciplinary team.
11. Goal orientation, self motivation, and drive to make a positive impact in healthcare.

Preferred Qualifications:

1. 4+ years full time employment or postdoctoral experience building and validating predictive models on structured or unstructured data.
2. Experience with traditional and deep learning approaches to survival modeling and subtyping.
3. Experience working with clinical cancer data (progression free vs overall survival, missing data etc.).
4. Understanding of CLIA/CAP validation protocols and how to bring scientific ideas to market as laboratory developed tests (LDT).
5. Strong peer-reviewed publication record.