EZEli Ziskin
@eliziskin
Data-driven Cognitive & Machine Learning Professional with interdisciplinary training.
United StatesWhat I'm looking for
I am a data-driven Cognitive and Machine Learning Professional with five years of experience designing and operationalizing AI/ML solutions in regulated life science environments. My interdisciplinary training in neuroscience, cognitive physics, and advanced analytics allows me to translate complex data into actionable insights at scale. I have a proven track record in Python, cloud-native MLOps, and generative AI, recognized for my rigorous scientific thinking and precision in execution.
At LOXO@LILLY, I lead the design and deployment of automated data pipelines and have built production-ready models that significantly enhance operational efficiency. My work has directly contributed to improving health outcomes by preventing costly reruns and accelerating compound screening. I thrive in collaborative environments, partnering with data scientists to containerize models and implement monitoring systems that ensure the integrity of our AI solutions.
My passion for continuous improvement drives me to explore innovative approaches in machine learning and data engineering. I am committed to leveraging my skills to contribute to projects that enhance human health and performance, while also fostering a culture of open science and reproducibility in research.
Experience
Work history, roles, and key accomplishments
Chemistry Laboratory Coordinator
LOXO@LILLY
Oct 2024 - Present (8 months)
Led the design and deployment of an automated data pipeline using Python and Azure Functions, ingesting over 10 GB/day of assay output and publishing feature-ready datasets. Built and productionized a Ridge regularized predictor with scikit-learn, achieving 93% recall in flagging out-of-spec batches. Partnered with data scientists to containerize models via Docker and orchestrate deployments with
Laboratory Assistant
LOXO@LILLY
May 2024 - Present (1 year 1 month)
Authored a suite of reusable Python ETL scripts that harmonized instrument logs across 12 assay types, standardizing schema and reducing downstream parsing errors by over 80%. Devised a lightweight FastAPI microservice exposing validated concentration response models to chemists through a React front end, enabling "insight as a service" in the lab.
Lead Neuroscience Technician
INOTIV
Jan 2024 - Present (1 year 5 months)
Directed data acquisition on preclinical rodent cognition studies; engineered a PyTorch-based CNN to classify behavioral endpoints from high frame rate video, achieving 95% F1. Collaborated with statisticians to deploy a reproducible analysis pipeline using Docker and Jupyter Hub, cutting report turnaround from 7 days to under 24 hours.
Psychometric Data Analyst / Research Associate
University Research & Consulting
Jan 2020 - Present (5 years 5 months)
Administered and analyzed large-scale cognitive assessments, leveraging mixed effects models in R/Python to uncover latent factors driving performance variance. Presented findings at the Society for Neuroscience conference, emphasizing quantitative validity and open science reproducibility.
Education
Degrees, certifications, and relevant coursework
UDUniversity of California Davis
Bachelor of Arts, Cognitive Science
Grade: 3.8/4.0
Studied Cognitive Science with a minor in Applied Mathematics. Completed an honors thesis focusing on Bayesian models of sensorimotor learning. Furthered education with postgraduate coursework in Machine Learning (Stanford CS229), Deep Learning (fast.ai), and Cloud ML Engineering (Coursera GCP specialization).
Availability
Location
United StatesAuthorized to work in
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