Data Scientist Fellow, Bioinformatics and Image Analysis

Axle • Bethesda, MD

3d • $90,000 - $100,000 • Hybrid

About The Position

Axle is seeking a Data Scientist Fellow for a hybrid research position supporting an autonomous laboratory in Bethesda. The Fellow will analyze data generated by biological experiments conducted on the BAB400 robot, whose imaging system is a Cytation 9. The primary data stream is microscopy imagery. The role involves building and operating image-analysis and bioinformatics pipelines, integrating environmental monitoring data, and delivering quantitative readouts to the autonomous system so it can inform subsequent experiments.

The Fellow will work closely with the Laboratory Automation Engineer and IDSS biologists and data engineers. The position combines reliable pipeline operations with research contributions, including developing new methods, publishing results, and presenting findings. The role also includes integrating available downstream omics data, maintaining data practices aligned with FAIR principles and NIH data management and sharing requirements, documenting procedures, and training staff.

Requirements

• Doctoral degree (PhD or equivalent) in bioinformatics, computational biology, biomedical engineering, computer science, physics, or a related quantitative field, normally completed within the last five years.
• Demonstrated experience analyzing microscopy or other biological image data, including segmentation and quantitative feature extraction.
• Strong Python skills, including scientific and imaging libraries such as NumPy, SciPy, pandas, and scikit-image or similar, and experience with at least one machine-learning framework such as PyTorch or TensorFlow.
• Strong grounding in statistics and experimental design, and experience validating analysis methods.
• Experience with Git, reproducible analysis practices, and a Linux or high-performance computing environment.
• Research output such as peer-reviewed publications, preprints, or released software.
• Ability to work in a close, iterative partnership with an automation engineer and biologists, and to explain methods and results clearly to both.
• Ability to obtain and maintain NIH/HHS security clearance and meet federal contractor suitability requirements.

Nice To Haves

• Experience with high-content or live-cell imaging systems, especially Agilent BioTek Cytation or Gen5 software.
• Experience with image-analysis tools such as CellProfiler, Cellpose, StarDist, napari, or ImageJ/Fiji, and deep-learning methods for segmentation and classification.
• Experience with stem cell, iPSC, organoid, or tissue-culture systems and their imaging.
• Experience with time-series or multimodal analysis, including sensor and environmental monitoring data.
• Experience with self-driving laboratories, active learning, or Bayesian optimization.
• Experience with single-cell omics analysis (scRNA-seq) and workflow tools such as Nextflow or Snakemake.
• Familiarity with laboratory orchestration software such as MADSci, and with Docker or Kubernetes.
• Experience with high-performance computing environments such as NIH Biowulf.

Responsibilities

• Develop and maintain automated analysis pipelines for Cytation 9 images, including quality control, illumination and background correction, segmentation, tracking, and feature extraction for cultures such as iPSC colonies and organoids.
• Define and validate quantitative measures of culture state and growth, such as confluence, colony morphology, differentiation markers, and viability, including uncertainty estimates for use by the autonomous system.
• Work with the Laboratory Automation Engineer to deliver analysis results to the workflow orchestration layer in a machine-readable format and within the timescale needed by the autonomous loop; define interfaces, failure behavior, and safeguards against decisions driven by bad data.
• Collect, clean, and align environmental monitoring data, including sensor streams such as temperature, CO2, O2, and humidity, with imaging results; analyze relationships with culture outcomes and flag drift, excursions, and sensor faults.
• Apply and develop statistical, machine-learning, and deep-learning methods for image and multimodal data, with attention to validation, reproducibility, and batch effects; use existing instrument software where appropriate and develop custom methods as needed.
• Integrate downstream omics data, including single-cell data when available, and link it to image-derived phenotypes.
• Work with IDSS data engineers to ensure images, derived measurements, and metadata are stored, versioned, and findable in accordance with FAIR principles and NIH data management and sharing requirements.
• Publish methods and results, present to NIAID and collaborators, document pipelines and standard operating procedures, and train staff to use analysis tools.
• Collaborate closely and iteratively with the Laboratory Automation Engineer, biologists, and data engineers, and communicate methods and results clearly.

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 for 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