As a data science consultant, I build end-to-end data science and machine learning solutions to solve the most complex problems. I have a blast combining advanced analytical techniques like deep learning, NLP algorithms, and predictive modeling with an understanding of business needs to bring insights to the teams I consult. With a unique background in applied statistics, program management, and psychological research, I bring a rare set of skills and perspectives to the table.
What I love about data science is that it has allowed me to do the following:
• Design and build the groundwork architecture for corporate analytics teams that enable data-driven business decisions and drive the future of the business.
• Identify a business's technical needs and present data solutions directly to product owners and corporate executives.
• Mine raw data and build visualizations to reveal insights into the health and business value-add of various products and services.
• Implement NLP deep learning models with a variety of distributed cloud computing tools to engineer productionalized automated pipelines that scale to big data.
My varied background has been a tremendous strength in my consulting work, especially in NLP text analysis problems. I'm well equipped to unravel the complexity of human emotion and linguistics to distill the voice of the customer from raw data. I've mined social media and public data sources to accomplish a number of NLP tasks including sentiment analysis, topic modeling, and designing chatbots fueled by deep learning.
Also, as a member of management in a mental health treatment program, I worked directly with some of the most extreme clients in the country to treat severely violent and self-harming behaviors. This taught me to calmly lead a team, communicate, and make decisions in life or death emergency situations.
About me:
Data is driving the future and I want to be one of the people that leads it. I love to make people laugh and I believe in making a cheerful environment for everyone around me. I enjoy all aspects of analytics, but I especially love solving problems related to NLP text analytics and AI/reinforcement learning. My need to keep learning keeps me hungry for the next big challenge, reach out to me!
Worked with product owners to provide data science consulting for product evaluation and reporting, leverage cloud services within Azure such as Databricks and SQL Server.
Engineering and maintaining end to end data pipelines, from API calls to visualizations, using distributed computing tools such as Spark.
Applied machine learning techniques such as clustering and NLP modeling to solve business needs and test hypotheses.
Developed sentiment analysis model to measure the emotional polarity of customer feedback from Reddit, App Stores, and other sources of text data.
Led team of 15 Behavioral Specialists to implement treatment plans and ensure safety of children with severe behavior disorders.
Managed program and responded to emergency situations during 24/7 campus on-call supervisor rotation.
Conducted qualitative analysis of textbook math problems to study the mental representation of fractions and decimals.
Guided participants through experimental procedures in a study on subconscious and implicit cognition.
Graduated highest in class across all of General Assembly’s campuses around the world.
Applied machine learning and tools such as Python, SQL, AWS cloud computing, Spark, and Git to solve a range of problems, including developing recommender systems, image recognition, NLP, and clustering.
Generated predictions with regression and classification models, e.g. gradient boost, neural networks & SVM.
Visualized data with tools like Tableau and Matplotlib and communicated results to non-technical audiences.
Conducted statistical analyses to extract features and to train and validate models such as linear regression.
Utilized business intelligence tools such as SAS to calculate mathematical metrics and perform A/B tests.
Studied experimental methodology and statistical hypothesis testing to publish research articles.
Conducted tests to measure psychometrics and draw conclusions about human behavior in controlled settings.
Utilized Thompson sampling algorithm to automatically choose the best advertisement as new data is acquired.
Used Bayesian reasoning to improve profits by employing best ad strategy as quickly as possible.
Engineered a convolutional neural network in Keras to detect exoplanet transit patterns 15x better than chance.
Sampled from NASA’s bulk data API, cleaned and processed data and determined neural network architecture.
Applied reinforcement learning artificial intelligence to calculate route between points in a warehouse.
Demonstrated methods for automating business processes to reduce expenses and improve efficiency.
Performed spatial and time series analysis to optimize mosquito population control efforts in Chicago.
Collaborated with other data scientists as a team, visualized results and presented cost-benefit analysis.
Implemented classification models and combined into ensemble to predict subreddit with over 94% accuracy.
Web scraped text from Reddit’s API to collect titles of posts and processed with NLP techniques in NLTK.
Compared statistical models, including support vector machine (SVM) regression, to predict housing sale prices.
Cleaned, processed, and selected features from data to apply machine learning algorithms.
Deep Learning
I'm fascinated with using machine learning and neural networks to solve complicated challenges such as pattern recognition, computer vision, and knowledge representation.
NLP
I think it's exciting to discover insights in text data with topic modeling, sentiment analysis, and other natural language processing practices.