Data Scientist Fellow — AI/ML

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).

Responsibilities

• Model development. Design and implement machine learning models that predict organoid development outcomes based on protocol parameters, environmental conditions, and molecular characterization data.
• In silico simulation. Develop in silico models that correspond to organoid growth, maturation, and characterize phenotype while identifying optimal conditions for reproducible organoid generation.
• Feedback systems. Establish feedback loops between experimental validation and computational prediction in order to iteratively refine and understand protocol standardization.
• Interdisciplinary collaboration. Work with experimental teams to design characterization and validation experiments and with bioinformatics scientists to integrate multi-modal datasets.
• Required Qualifications
• Education. Doctoral degree (PhD) in computer science, engineering, applied mathematics, or a related field, with demonstrated experience in AI/ML model development.
• Programming. Strong programming proficiency in Python, R, or comparable languages, together with experience using machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Applied experience. Prior experience applying computational methods in biological laboratory settings, including working knowledge of experimental design principles and biological data types.
• Preferred Qualifications
• Experience with organoid systems or tissue engineering applications.
• Knowledge of differential equations and systems biology modeling, and experience with deep learning approaches for biological applications.
• Familiarity with cloud computing platforms and containerization technologies.
• Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
• The diversity of Axle’s employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.
• Accessibility: If you need an accommodation as part of the employment process please contact: careers@axleinfo.com
• This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location.

Conditions

$90,000 - $100,000 USD

Benefits

• 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.