Brooks Running:• Built a pipeline to ingest survey data and join to wearable fitness tracker data • Pipeline cleaned a highly over parametrized dataset• Multiple models (Decision Tree, Random Forest) were built on the data with a focus to understanding the impact of different input variables (permutation importance, and SHAP) on runners Marathon Performance• Checked for statistically significant relationships (Chi-square, t-test) in consumers brand selectionBeam Suntory (Jim Beam):• Built forecasting models for consumer consumption, with a 6 month time horizon using Random Forest, XGBoost, LightGMB, with grid search hyper-parameter tuning for multiple markets.• Provided a feature importance deep dive using model specific methods and SHAP analysis• Solutions were built using big query, and Google Cloud Product (GCP)Service Now:• Implemented Large Language Model (LLM) performance using innovative prompt engineering with few-shot learning for Llama 2 & 3, Minstrel, and StratChat2.• Streamlined the LLM development process by crafting a Retrieval Augmented Generation (RAG) workflow, enhancing efficiency.• Established a robust framework for ongoing LLM experimentation, ensuring continuous improvement and model optimization.• Utilized Visual Studio, Azure, Docker, NLTK, spaCy, LangChain, chroma, sacrebleu to classify, extract, and search on unstructured text data

• Data Gathering and Storage Strategy• Implementing code conversion from R to pySpark on DataBricks• Unit testing python code for implementation• Creation and management of a custom python library• Creation of custom feature engineering• Dimension reduction for over parameterized data• Sales to potential customers• Direction, spec, and creation of tableau dashboards• Prompt engineering for a LLM used for generative AI in operational business use case• Productionalize LLM for business use case via python azure function - http trigger

• Sr. Data Scientist / Tech Lead / Project Manager for all Data Science / Business solutions• Build end to end data science pipelines for ML models across verticals, including product, sales and operations• Explore, clean and create data in preparation for modeling• Build proof of concept, and scale models• 3 models utilized in business decision making and run weekly• Manage and monitor models life-cycles• Leveraged facebook prophet and SQL for time series model development & refinement.• Simplified several hundred lined SQL query manually to optimize for readability as well as computation• Built a fraud detection, application grading, and revenue prediction modelsTech Stack: Python, AWS, EC2, Sagemaker, Snowflake, Docker, Tmux, Linux, SSH, MySQL, SQL, TensorFlow

• Reserve meeting space• Publish lectures to YouTube• Book speakers for quarterly meetings• Network with data specialists• Established and grew group from 100 people to > 500 individuals

• Fraud detection across 350 features and millions of records• Built, trained, and maintained a fraud detection model that increased detection by 82% when compared to prior model• Built SQL queries to access a 6 billion record database via snowflake and pythonTech Stack: AWS, EMR, S3, Python, PySpark

• Technical Project Manager of Data Science• Pre-sales pitches• Set direction and tone for data science on project• Deploy similar analytical methods onto multiple locations in pheonix, hive, hadoop, influx and greenplum• Helped grow a POC contract from a $10,000's to a few million• Contracts include an entertainment vertical based on a mouse,NASA, and Michael's craft store.• Pioneered real-time ML using Greenplum, Linux, & Python, focusing on predictive maintenance on time series data.• Managed a team of 2 data scientists & 2 data engineers, orchestrating projects & ensuring successful execution.• Directed technical projects for clients, maintaining strong customer relationships as an engagement manager.• Successfully operationalized natural language processing solutions for open text data.• Designed & implemented pipelines for predictive maintenance algorithms, utilizing LSTM, & autoencoders, incorporating neural networks & Python.

• Providing data analysis expertise for a variety of customers and data types: Clientele includes International theme parks, NASA, IBM and Michael's• Basic understanding of Linux, Couchbase, Greenplum, and Python• Fluent in R and analytical techniques• Analyzed time series big data at the millisecond level• Creation of in database analytics for quicker computation• Natural Language Processing, Predictive Maintenance, Neural Networks, Supervised & Unsupervised Learning

• Manage & Analyze nuclear power plant data in SAS & R• Wrote >15 peer reviewed articles as first author• Modeling data using GLM, MCMC, Simulations and other Machine Learning techniques• Applied risk assessment and human behavioral modeling for risk analysis• Lead and co-writing milestone reports• Basic understanding of SQL, mathematica, matlab, JMP, C#• Manager of The Data Analysis Office, Post-Irritation Data Stream Manager, Statistical Point of Contact for the Nuclear Regulatory Commission, Lead Statistician

• Employed AB testing for cardiology surgery techniques • Utilized SAS & R programming techniques to retrieve data • Project management skills to complete projects in a timely manner• Collaborated on data collection, and archival• Executed Kaplan-Meier & Cox Survival models for analysis of clinical trials• Utilize statistical knowledge, programming techniques, and project management skills in data management

• Planned and ran review sessions for 40+ students• Tutored for classes with 150+ students• Lectured when instructors were out of town• Designed and graded exams in a timely fashion for 150+ students• Instructed students on the use of R, SAS, and mini tab• Wrote lesson plans• Aligned grades to a normal distribution• Teacher’s assistant for Dr. Lee, Dr. Lowery, Alex Suchar, and Renae Shrum

• Assisted in the design of experiments and surveys• Advised customers on statistical methods and analyses• Provided expertise in research• Created SAS and R coding to aid in answering research questions• Analyzed data using many uni-variate and multivariate techniques• Utilized simulation methods to validate results

• Applied effective time management to complete projects• Completed research plot maintenance• Collected soil samples, seeds, and other metrics on plant development• Attention to detail needed to be maintained to complete several tasks• Utilized statistical skills by analyzing data collected • Worked for Joan Campbell and Traci Rauch in invasive weed research

• Instructed and managed 30+ individuals in a group lesson setting• Organized other coaches and students while on the ice• Placed students in the proper level so that they can excel• Organized and publicized events

• Utilized my charismatic ability• Presented to 1000+ customers• Organized and coordinated events with professionals and businesses• Executed promotions without error• Always shown up early to set up for promotions• Have worked for many companies multiple times: • PS Sterns (NibMor Chocolate) • Method Models (Time Warner Cable) • 360 Promotions (Bird Dog Whiskey) • WeGoLive (CheerWine) • Victory Promotions (Keurig)

• Provided technical support for computers• Repaired and manage printers for the largest computer lab on campus• Gained experience with Apple and Microsoft operating systems• Maintained an organized work space• Always reported early• Filled in for other students

• Only allowed skaters on the list on the ice• Maintained order of ice skating programs• Implemented knowledge of rules and regulations of Suburban Ice Arena• Managed order in chaotic situations• Always showed up early• Never missed a day, and filled in for others

• Complete research during one academic year• Communication between Emydoidea blandingii (an endangered turtle) with Dr. Rowe• Researched with two other students on 200+ juvenile turtles• Testing the correlation between the coloration of the environment and their skin/shell pigment• Utilized a spectral analysis machine to record what wavelength of light is reflected off the turtle• Live captured adult turtles, • Recorded turtle locations using telemetry• Help raise juvenile turtles• Observed and scored turtle behavior• Attention to detail was utilized• Maintained a rigorous schedule

• Effective time management• Compiled information in departmental research projects• Acquainted with printer protocol• Always showed up on time and never missed a day• Advertisement for departmental events

• Coordinated with other professionals to complete marketing events• Set up displays for products• Maintained a clean and organized work space• Effectively prepared delicious food • Created connections with the customer• Interacted and handed out samples of products