Data Scientist Fellow - AI/ML

Job Description

(ID: 2026-3885)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We Offer:

• 100% Medical, Dental & Vision Coverage for Employees

• Paid Time Off and Paid Holidays

• 401K match up to 5%

• Educational Benefits for Career Growth

• Employee Referral Bonus

• Flexible Spending Accounts:

• Healthcare (FSA)

• Parking Reimbursement Account (PRK)

• Dependent Care Assistant Program (DCAP)

• Transportation Reimbursement Account (TRN)

Position Overview

The Data Scientist Fellow will develop computational models to predict and optimize organoid growth and differentiation protocols in support of the SOM Center. The incumbent will apply advanced machine learning techniques to organoid standardization and contribute to the development of predictive models for tissue engineering applications.

Key Responsibilities

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Model development. Design and implement machine learning models that predict organoid development outcomes based on protocol parameters, environmental conditions, and molecular characterization data.

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In silico simulation. Develop in silico models that correspond to organoid growth, maturation, and characterize phenotype while identifying optimal conditions for reproducible organoid generation.

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Feedback systems. Establish feedback loops between experimental validation and computational prediction in order to iteratively refine and understand protocol standardization.

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Interdisciplinary collaboration. Work with experimental teams to design characterization and validation experiments and with bioinformatics scientists to integrate multi-modal datasets.

Required Qualifications

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Education. Doctoral degree (PhD) in computer science, engineering, applied mathematics, or a related field, with demonstrated experience in AI/ML model development.

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Programming. Strong programming proficiency in Python, R, or comparable languages, together with experience using machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.

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