Career Profile
Lead Software Engineer with a PhD in Neuroscience, currently spearheading healthcare technology development at Quantivly. Experience managing remote development teams and architecting scalable platforms that transform complex medical data into actionable insights. Deep expertise in Python, Django, and full-stack development, combined with strong research background in computational neuroscience and neuroimaging analysis. Passionate about open-source software and bridging the gap between cutting-edge research and practical industry applications.
Professional Experience
Quantivly
- Continue leading Quantivly Hub platform development with expanded team management responsibilities
- Manage distributed development team of 5 engineers across backend, frontend, and DevOps functions
- Oversee production deployment and scaling of the platform architecture
- Drive strategic development methodology improvements and project planning frameworks
- Balance hands-on technical contributions with leadership and organizational responsibilities
- Led development of the Quantivly Hub platform from initial design through implementation
- Implemented Django/React architecture with JSON-RPC over WebSocket communication for real-time data interaction
- Designed automated CI/CD workflows and infrastructure deployment processes
- Expanded technical expertise in Django, GraphQL, React, TypeScript, and security best practices
- Participated in team growth initiatives including interviewing, onboarding, and mentoring new engineers
- Transitioned to project management responsibilities while maintaining individual contributor work
- Contributed to architecture and development of radiology data intelligence platform
- Developed CLI tools for automating development workflows and deployment processes
- Improved stack documentation and development setup procedures
- Migrated company codebase from Bitbucket to GitHub, including workflow optimization
SpotItEarly
- Collaborated on data collection and validation processes
- Developed signal processing algorithms for early cancer detection
- Created a robust data pipeline for processing and analyzing experimental data
- Generated informative reports and visualizations to support research findings
Tel Aviv University
Taught Python programming fundamentals to neuroscience graduate students, focusing on data analysis, visualization, and research applications.
Instructed graduate students in neuroscience-oriented machine learning concepts, tools, and practical applications for brain data analysis.
Enhanced Flow Image Correlation Spectroscopy (FLICS) analysis implementation and developed an intuitive application for running analyses and visualizing results.
Guided undergraduate and graduate students through hands-on neuroanatomy laboratory sessions and practical learning experiences.
Education
Explored predictive modeling of subject attributes using MRI-derived parameters. Specialized in computational neuroscience, neuroimaging analysis, and machine learning applications in brain research.
Received full four-year scholarship from the Adi Lautman Interdisciplinary Program for Outstanding Students. Completed diverse coursework across multiple faculties, from medical anatomy to educational theory. Graduated with honors (94.75 final grade) with M.Sc. thesis on cortical layer distribution analysis using inversion-recovery MRI.
Open Source Projects
Key projects developed as part of research efforts and open-source contributions. For additional contributions, see my GitHub profile.