Neal has a professional track record of success over the past decade, working with various clients. For example, he's improved monthly item sales by 10% to 40% by implementing a machine learning model to predict customer demand. Neal is looking forward to helping more clients achieve their goals through the use of data science and technology.
Worked on LLM agent for automating support. Integrated state-of-the-art frameworks for Large-Language-Model agents such as Language-Agent-Tree-Search and GraphRAGUniversity
Developed and implemented Lockstep, a novel time-series segmentation approach to detecting and mitigating aggressive automation, which flags over 2M events per day.
Developed a novel algorithm for root cause analysis, hereby reducing 22 man hours per day of analyzing anomaly alerts. Deployed to production as a Flask API service, serving 100+ requests per day.
Developed a proof of concept and deployed to production an unsupervised neural network for the detection of synthetic keyboard telemetry. The model had demonstrated its efficacy in eliminating 6 million bot attempts per day with a popular customer which had been critically endangering their operations.
Through extensive feature engineering the model interference time was brought down by 100 fold.
Deployed an AB testing platform to examine the impact of customer setting alterations.
Developed an algorithm for geolocalization and size estimation of street objects.
Prevented cybersecurity attacks using anomaly detection algorithms, including isolation forest and robust autoencoders.
Developed object detection/localization using DenseNet and YOLO.
Developed a proprietary algorithm for geolocalization and size estimation of street objects.
Mentored junior data scientists.
Designed and executed experiments to understand the effects of variables on the system.
Generated and evaluated biophysical data based on purity, stability, binding, and specificity.
Used artificial neural network package, NETMHC, to predict the existence of peptide drug targets.
Predicted customer churn through machine learning.
Led label inference and semi-supervised machine learning in order to determine customer presence.
Improved customer conversion by predicting merchant attribute.
Created a gradient-boosted tree model that predicted the outcomes of cold-calling based on customer attributes. The model offered a 150% improvement.
Through the correct use of data structures, it was possible to parse over 4GB of data to extract text and ratings. Afterward, through the use of natural language processing (NLP) and SGDRegressor, it was possible to predict the rating of user reviews through semantic language. A data pipeline was created for the transformation and model fitting of the data.
VGG-16, a convolutional neural network model, was adapted and trained upon ~20,000 images to better predict real estate listing prices. This initial study increased the explained variance metric by 16%, demonstrating its viability as a proof of concept.
