About the role#
The Data Scientist Fellow will turn data from an autonomous laboratory in Bethesda into actionable results. You will work directly with the Laboratory Automation Engineer and biologists to build and run bioinformatics and image analysis pipelines. This is a hybrid research position where you will contribute new methods, publish findings, and ensure analysis runs reliably for the autonomous loop.
What you'll do#
- Develop and maintain automated image analysis pipelines for the Cytation 9 system, including quality control, segmentation, tracking, and feature extraction.
- Define and validate quantitative metrics for culture state and growth to serve as decision inputs for the autonomous system.
- Deliver analysis results to the workflow orchestration layer in machine-readable form and on a usable timescale.
- Define interfaces and failure checks to prevent bad data from driving decisions.
- Collect, clean, and align environmental monitoring data with imaging results to analyze relationships between conditions and outcomes.
- Flag environmental drift, excursions, and sensor faults.
- Apply and develop statistical, machine learning, and deep learning methods for image and multimodal data.
- Integrate downstream omics data, including single-cell data, and link them to image-derived phenotypes.
- Work with data engineers to ensure images and metadata are stored and findable in line with FAIR principles and NIH requirements.
- Publish methods, present to NIAID and collaborators, document procedures, and train staff on analysis tools.
What you'll need#
- Doctoral degree (PhD or equivalent) in bioinformatics, computational biology, biomedical engineering, computer science, physics, or a related quantitative field, completed within the last five years.
- Demonstrated experience analyzing microscopy or biological image data, including segmentation and feature extraction.
- Strong Python skills, including scientific libraries like NumPy, SciPy, pandas, and scikit-image.
- Experience with at least one machine learning framework such as PyTorch or TensorFlow.
- Strong grounding in statistics, experimental design, and method validation.
- Experience with Git, reproducible analysis practices, and working in a Linux or high-performance computing environment.
- A record of research output such as peer-reviewed publications, preprints, or released software.
- Ability to work in a close, iterative partnership with automation engineers and biologists.
- Ability to obtain and maintain NIH/HHS security clearance and meet federal contractor suitability requirements.
- Preferred: Experience with high-content imaging, deep learning for segmentation, stem cell or organoid culture, time-series analysis, self-driving labs, or single-cell omics.
Location & details#
- Location: Bethesda, Maryland
- Modality: Hybrid
- Employment type: Full-time
- Term: Rolling
About Axle
Axle is a software development company that provides a universal API for insurance data. The business allows organizations to verify insurance information and track coverage status. It maintains offices in Atlanta and New York City. The company operates with a team of two to ten employees.
How to get in at Axle
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