Data Scientist Fellow — AI/ML

Axle • Bethesda, MD

14h

About The Position

Axle is a bioscience and information technology company supporting translational research, biomedical informatics, and data science applications. The Data Scientist Fellow will develop computational models to predict and optimize organoid growth and differentiation protocols in support of the SOM Center. The role applies advanced machine learning to organoid standardization and contributes to predictive models for tissue engineering applications.

The Fellow will design and implement machine learning models based on protocol parameters, environmental conditions, and molecular characterization data; develop in silico models of organoid growth, maturation, and phenotype; and identify conditions for reproducible organoid generation. The role also establishes feedback between experimental validation and computational prediction and collaborates with experimental teams and bioinformatics scientists to design experiments and integrate multi-modal datasets.

Requirements

• Doctoral degree (PhD) in computer science, engineering, applied mathematics, or a related field.
• Demonstrated experience in AI/ML model development.
• Strong programming proficiency in Python, R, or comparable languages.
• Experience using machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Prior experience applying computational methods in biological laboratory settings.
• Working knowledge of experimental design principles and biological data types.

Nice To Haves

• Experience with organoid systems or tissue engineering applications.
• Knowledge of differential equations and systems biology modeling.
• Experience with deep learning approaches for biological applications.
• Familiarity with cloud computing platforms and containerization technologies.

Responsibilities

• Design and implement machine learning models to predict organoid development outcomes using protocol parameters, environmental conditions, and molecular characterization data.
• Develop in silico models of organoid growth and maturation and characterize phenotype to identify optimal conditions for reproducible organoid generation.
• Establish feedback loops between experimental validation and computational prediction to iteratively refine and understand protocol standardization.
• Collaborate with experimental teams to design characterization and validation experiments.
• Collaborate with bioinformatics scientists to integrate multi-modal datasets.

Benefits

• 100% medical, dental, and vision coverage for employees.
• Paid time off and paid holidays.
• 401(k) match up to 5%.
• Educational benefits for career growth.
• Employee referral bonus.
• Flexible spending accounts, including healthcare (FSA), parking reimbursement (PRK), dependent care assistance (DCAP), and transportation reimbursement (TRN).

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree