(Senior) Machine Learning Scientist, Spatial Transcriptomics

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients at the right time. 

We are seeking a skilled and innovative (Senior) Machine Learning Scientist to join the Cell Imaging team. As a Senior Machine Learning Scientist, you will perform complex computational analyses and develop algorithms for advancing cancer precision medicine for patients across the Tempus network. You will develop and apply best-in-class machine learning methods to represent, analyze, and interpret spatial transcriptomics data in conjunction with Tempus’s multimodal patient dataset. The ideal candidate will possess strong applied machine learning skills, experience with high-dimensional genomic data, including single-cell and spatial transcriptomics, and the ability to communicate complex findings to various stakeholders.

Responsibilities:

Scientific:

• 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.

Collaboration: 

• 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

Continuous Improvement:

• 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.

Required Qualifications:

• Education: PhD degree in computational biology, biostatistics, statistics, or any quantitative field with a strong statistical analysis and machine learning background.
• Experience: 2+ years leveraging genomic and multimodal data with machine learning approaches to address questions in complex diseases, especially cancer.
• Technical/Scientific Skills:Experience working with genomics data, including spatial or single-cell transcriptomics.Breadth and depth knowledge of machine learning algorithms and best practices.

• Experience developing, training, and evaluating deep-learning models using public deep learning frameworks (e.g. PyTorch, TensorFlow, and Keras).
• Strong programming skills and proficiency in Python and respective packages for computational biology and machine learning.
• Knowledge of best practices for code development, documentation, testing and deployment patterns.

• Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences. Comfort in a client-facing role.

Preferred Qualifications:

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

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