Responsible for designing end to end machine learning architectures across diverse fields including signal processing, maritime identification, fraud detection and stylometry. •Developing ground-station reception forecast utilizing Bayesian hierarchical models in TensorFlow Probability. •Lead data scientist on stylometry product using NLP techniques, implementing tokenizers in spaCy, integrating pipelines and developing machine learning models in scikit-learn. .• Implemented deep learning model in PyTorch to classify different font types for detection of counterfeits on microchips and identification of different nautical vessels in harbors. • Designed end to end machine learning system with offline batch training of anomaly detection algorithms and online serving of containerized models for inference utilizing ElasticSearch, PostGreSQL, Kafka, NiFi, and TensorFlow.

• Created statistical models that predict chemical product quality, decreasing product failure rate and analytical processingtime by 83%.• Consolidated data architecture into an automated Python data pipeline/ETL to capture new chemical processes and streamlined data analysis.• Mentored senior scientists to emphasize focus on new product development.• Led department overhaul with a focus on developing new products versus service based. Monitored progress using KPIs for product development department and reduced overhead on projects.• Created Dashboards pulling data from the chemical process database, project management API, and SalesForce API to create an integrated reporting tool for department KPIs and product quality performance using Python, R and PowerBI.• Spearhead new research initiative with Business Development and Senior Scientists to prioritize new thin film development to improve products.

• Successfully managed multi-million dollar Phase 1 project for Fortune 500 customer to develop novel alumina thin films on cathode battery particles from project initiation through project execution. Reported to executive team and key stakeholders on project status and effectively implemented requirements as team lead.• Implemented factorial design of experiments using JMP on in-house designed equipment (see patents) and determined crucial factors/optimal process parameters to scale up production efficiently while still providing a product with optimal product performance.• Proposed and executed laboratory upgrade, including budget, plans and execution to improve internal testing capability.• Programmed data pipeline package using object orientated programming in Python to perform integration of analytical tools with MySQL and AWS.• Trained and mentored new development scientists.

• Optimized process parameters using continuous improvement to cut price and production times while maintaining quality of nanocoatings on powders in fluidized bed reactors.• Developed statistical test to ensure quality product is shipped after coating decreasing product failure rate.• Designed and built custom database for data warehousing of all internal nanocoating runs.

Developed roll-to-roll atomic layer deposition for porous lithium ion electrodes.

• Developing new molecular layer deposition (MLD) films with enhanced mechanical properties using metal precursors and organic precursors• Direct deposition of MLD films on carbon nanotubes• Annealing of MLD films to create porous membranes• Performing mechanical testing of newly developed MLD films • MLD for sacrificial layers used in MEMs/NEMs fabrication

Worked on large batch ALD particle coatings in a fluidized bed reactor.