Michael Melnick Email & Phone Number

Truro, Gb

Rochester, New York Metropolitan Area

Principal Data Scientist primarily leading work analyzing hyperspectral imagery for mineralogical identification and quantification. Extensive work in serverless end-to-end analytical cloud pipelines with multiple worldwide teams in order to create a fully functional, state of the art data application from ingestion through cleaned and vetted results for internal experts and external clients.

Rochester, New York, United States

Senior Data Scientist tasked with modernizing current products and analytical pipelines to serverless cloud architectures for scaleable analyses. Leading generation of novel analyses for data science based services across projects within the company.

Rochester, New York Area

Responsible for insights into complex data analyses including Bayesian statistics, super-computer-reliant operations, fMRI analysis, and novel analysis methods. Also contributing to ongoing publications through primary and secondary analysis, and manuscript editing and writing.

St. Helena, California

Responsible for leading, designing and building end to end data collection for home network devices. - Lead development of Python-based, containerized firmware for benign man-in-the-middle data collection from home network device- Produced reliable cloud-based, serverless architecture for ingesting, cleaning, and analyzing captured network packet data (PCAP)- Developed novel analytical methods for detecting network packet anomalies, including intellectual property protection via patent filings for relevant methodologies- Coordinated and managed frontend development and integration with backend services for user experience of home network data.

Oklahoma City Metropolitan Area

- Led a small team of data scientists to produce a functional and accurate proof of concept model of patient fitness for 4 classes of Type-II diabetic treatments utilizing CERNER EHR records in big data analytic pipelines (Python-based Dask and Vaex)

St. Helena, Ca; New York, New York

Performed novel research on modern analysis techniques in quantitative finance. Kept the rest of the company abreast of current research and statistical foundations in their applicability to our work in financial tech. Spearheaded the creation of a scaleable time series machine learning inference application for use in ensembling multiple predictive methodologies into more robust predictions. Lead efforts to design and implement a novel financial modeling domain specific language.

Doctor Of Philosophy - Phd, Brain & Cognitive Sciences

Dissertation titled Neural efficiency and processing: understanding visual perceptual processing using transcranial electrical stimulation.

Bachelor Of Science - Bs, Neuroscience