Scientist I, Machine Learning

About the Job

The Scientist I, Machine Learning contributes to research, implementation, and validation of computational methods for FMI’s internal Cancer Genomics Research group. This position supports the development of machine learning algorithms and data pipelines applied to histopathology images in conjunction with genomic sequencing data and clinical outcomes. The Scientist I contributes to workflows that may include the investigation and identification of novel biomarker signatures, discovery of novel cancer genomics findings, support of critical data science partnerships, and improvement to FMI’s operational pipelines.

Key Responsibilities

1. Perform machine learning and deep learning on large-scale structured and unstructured datasets (images, free text) to extract biological insights.
2. Develop data pipelines, infrastructure, and computational tools for large-scale image analysis.
3. Provide scientific expertise and support for internal teams and external collaborators.
4. Conduct novel cancer genomics research using both public and internal datasets.
5. Prepare reports and presentations to communicate results in group meetings.
6. Present novel findings via abstracts or manuscripts.
7. Other duties as assigned.

Qualifications

Basic Qualifications

1. Bachelor’s Degree in Computer Science, Bioinformatics, Computational Biology, Engineering, or other similar quantitative discipline and 3+ years of work experience in relevant field; OR
2. Master’s Degree in Computer Science, Bioinformatics, Computational Biology, Engineering, or other similar quantitative discipline and 2+ years of experience in relevant field.

Preferred Qualifications

1. Ph.D. degree in Computer Science, Bioinformatics, Computational Biology, Engineering, or other similar quantitative discipline.
2. Knowledge of cancer biology and cancer genomics.
3. Experience with histopathology analysis.
4. Strong experience with deep learning (particularly convolutional neural networks) methods and frameworks (Tensorflow, PyTorch, etc.) and a strong understanding of their mathematical foundations.
5. Intermediate proficiency or higher in object-oriented programming with Python, Java, or C++.
6. Experience with traditional machine learning methods and packages (e.g. sklearn) and a strong understanding of their mathematical foundations.
7. Experience with distributed processing and computation (Spark, Horovod, job scheduling, etc.) for large-scale datasets.
8. Familiarity with using cloud compute providers (AWS, GCP, etc.).
9. Previous authorship/co-authorship of relevant work.
10. Strong communication and teamwork skills to work effectively in a flexible, cross-functional environment.
11. Understanding of HIPAA and the importance of patient data privacy.
12. Commitment to reflect FMI’s values: Patients, Passion, Innovation and Collaboration.