Data Scientist Fellow – Organoid AI/ML Modeling

Axle; Bethesda (MD); On-site; USD 90,000 - 100,000; Full time

Benefits offered by this job

PTO & Holidays

401K match

Education benefits

Referral bonus

Healthcare FSA

Parking reimbursement

DCAP

TRN

Job summary

Axle is a bioscience and information technology company applying AI/ML to translational research. We seek a Data Scientist Fellow to develop predictive models for organoid growth and standardization, collaborating with cross-functional teams in a fast-paced environment.

The role emphasizes model development, in silico simulations, and iterative feedback between experiments and computation to accelerate discoveries in tissue engineering.

Qualifications

• Doctoral degree (PhD) in computer science, engineering, applied mathematics, or a related field, with demonstrated experience in AI/ML model development.
• Strong programming proficiency in Python, R, or comparable languages, with experience using machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Applied experience implementing computational methods in biological settings and with experimental design principles.

Responsibilities

• Model development: Design and implement ML models predicting organoid development outcomes from protocol parameters and data.
• In silico simulation: Develop models of organoid growth and maturation to identify optimal reproducible conditions.
• Feedback systems: Create loops between experimental validation and computational prediction to refine standardization.
• Interdisciplinary collaboration: Work with experimental and bioinformatics teams to integrate multi-modal datasets.

Skills

Python programming

AI/ML modeling

Data analysis

Team collaboration

Education

PhD in CS/Engineering/Applied Math

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Axle is a bioscience and information technology company applying AI/ML to translational research. We seek a Data Scientist Fellow to develop predictive models for organoid growth and standardization, collaborating with cross-functional teams in a fast-paced environment.

The role emphasizes model development, in silico simulations, and iterative feedback between experiments and computation to accelerate discoveries in tissue engineering.