Senior Machine Learning Scientist

Summary

Join Tempus as a Senior Machine Learning Scientist and contribute to advancements in cancer precision medicine. You will develop and apply machine learning methods to analyze spatial transcriptomics data, collaborating with cross-functional teams. This role requires a PhD in a quantitative field, 2+ years of experience with genomic data and machine learning, and strong programming skills in Python. Preferred qualifications include experience with deep representation learning and large-scale imaging data. The position offers a competitive salary and a full range of benefits, including incentive compensation, restricted stock units, and medical benefits.

Requirements

• Possess a PhD degree in computational biology, biostatistics, statistics, or any quantitative field with a strong statistical analysis and machine learning background
• Have 2+ years leveraging genomic and multimodal data with machine learning approaches to address questions in complex diseases, especially cancer
• Have experience working with genomics data, including spatial or single-cell transcriptomics
• Possess breadth and depth knowledge of machine learning algorithms and best practices
• Have experience developing, training, and evaluating deep-learning models using public deep learning frameworks (e.g. PyTorch, TensorFlow, and Keras)
• Possess strong programming skills and proficiency in Python and respective packages for computational biology and machine learning
• Have knowledge of best practices for code development, documentation, testing and deployment patterns
• Possess excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences. Be comfortable in a client-facing role

Responsibilities

• Research and develop best-in-class machine learning models to advance the state-of-the-art in spatial transcriptomics analytics
• Support exploratory research, development and validation studies on Tempus’s multimodal clinical, imaging, and sequencing datasets to drive innovations in drug development and clinical testing
• Build and deploy robust, industrial scale machine learning models and data pipelines for structured and unstructured data
• Work closely with other cross-functional teams across the R&D and broader Tempus organization (product engineering, operations, clinical genomics labs, medical, science, data science, etc) to communicate research and integrate work plans and approaches
• Document, summarize, and present your findings to a group of peers and stakeholders
• Stay current with industry trends, best practices, and advancements in spatial biology research
• Apply this knowledge to enhance research methodologies and improve overall research quality on the team

Preferred Qualifications

• Have a PhD with 2+ years of work experience
• Have experience in developing and applying deep representation learning methods (e.g. generative models, contrastive learning, and graph-based methods)
• Have experience working with large-scale imaging data and formats (e.g., pathology WSIs, high throughput optical microscopy) and with modern computer vision techniques
• Possess extensive knowledge in biology, especially medical or oncology-related
• Have experience with version control (GIT) and collaborative software development and testing
• Have experience working with Docker containers and cloud-based compute environments (e.g., AWS or GCP)
• Have experience in a late-stage startup environment
• Be goal-oriented, self-motivated, and driven to make a positive impact in healthcare

Benefits

• Incentive compensation
• Restricted stock units
• Medical and other benefits