Made at Metis Graduate Directory

In this free directory, you will find resumes, bios, and final project presentation videos (4-8 minutes in length) for each Metis data science bootcamp graduate actively seeking employment.

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SPRING 2022

David Doberne, Data Science & Machine Learning

David (Dayv) Doberne grew up a baseball fan in the San Francisco Bay Area, and experienced the Moneyball A's revolution firsthand during his formative years. This deeply ingrained in him an appreciation for looking past conventional wisdom and toward solutions rooted in statistics and observable trends. Dayv graduated with degrees in Biology and Music Performance from the Oberlin College & Conservatory, and is now equipped with tools sharpened by the Data Science & Machine Learning program at Metis to sculpt solutions out of problems big and small. Outside of his professional life, Dayv makes a point to watch Moneyball once a year, and also enjoys rock climbing and strategy games.
David
Doberne, Data Science & Machine Learning
The Filthiest (Baseball Pitching)
Big Data/Data Engineering / Classification / Cloud Computing (AWS/Google Cloud)
The Filthiest uses a Random Forest Classifier model to sift through web scraped data from MLB pitches and return the best highlights via a Streamlit app.
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Erica Stevenson, Data Science

Erica Stevenson is an accomplished scientist who is fascinated by the complex data of human behavior and the natural world. She discovered her curiosity for data science while working with mass spectrometry data in R, which prompted her to pursue further studies with the project-based bootcamp, Metis. Erica has Masters degrees in Organic Chemistry and Chemical Engineering, as well as 10 years of experience working in scientific fields. Outside of work, she is a violinist, dancer, and consciousness researcher. She looks forward to continued learning and new challenges as she pivots her career.
Erica
Stevenson, Data Science
Music Information Retrieval - Genre Classification
Big Data/Data Engineering / Regression / Classification
This project used the Free Music Archive (FMA) dataset, Music Information Retrieval (MIR), and classification models to predict the genre of a song based on its high dimensional spectral features.

Marjan Rezvani, Data Analytics

Marjan Rezvani graduated from the City University of New York in 2021 with a Master's Degree in Data Science and Engineering. She has experience in analyzing data, developing Machine Learning, and Statistical models to solve complex problems. She has a passion for using her skills to leverage data to aid companies with their business strategies, and explore exciting new fields developing from within data science, but on the side, she also enjoys panting. To get in touch with her, email her on [email protected]
Marjan
Rezvani, Data Analytics
Predicting Housing Prices
Anomaly Detection / Big Data/Data Engineering / Regression
Collected information on properties from Century21 pertaining to several states of the America by leveraging BeautifulSoup to collect and parse data based on specific search queries. Analyzed 12 unique property features for 1800 properties.

Matt Edrich, Data Science & Machine Learning

Matt grew up in the sunny suburbs of Denver, Colorado, and feels incredibly fortunate to have had so much access the outdoors in his formative years. The natural world is an enormous piece of his identity: he likes to play and rest in it, and strives to protect it through his professional efforts. Matt combined his academic background in ecology and sustainability with his natural communication and storytelling talents to excel as a professional naturalist and biodiversity steward before enrolling with Metis. Newly empowered with a broad array of data science skills and tools, he hopes to leverage AI to work towards a zero-carbon, biocentric world. In his free time, he loves to tinker, craft, climb rocks, travel, read, and spend time in nature.
Matt
Edrich, Data Science & Machine Learning
Captioning, Computerized
Neural Networks / Classification / Natural Language Processing
Deep learning tools and NLP techniques to automatically caption images.

Melanie Ackerman, Data Science & Machine Learning

Melanie is a fast-learning data scientist with a strong economics and math background who is passionate about finding data-driven solutions to policy failures. She currently collaborates with economists at the Cornell Dyson School on regression analysis in applied economics. Previously, she worked on Capitol Hill as a Public Policy Data Analyst for the Joint Economic Committee Democratic Staff where she wrote a report urging Congressional investment in child care, which increased legislative support for the struggling sector, as well as a policy brief analyzing disparate internet access across the U.S. While she loved this work, she wanted to make a larger impact with her technical skills, which led her to Metis. As a former college athlete, she loves watching and playing basketball, hiking, and reading outside of work.
Melanie
Ackerman, Data Science & Machine Learning
Grey's Anatomy Episode Recommender
Recommender Systems / Natural Language Processing / Tableau/Dashboards
This project uses web scraping, NLP, and topic modeling to produce an episode recommender app based on topic for Grey's Anatomy fans who want to rewatch episodes but don't know where to start.

Nate DiRenzo, Data Science & Machine Learning

Nate DiRenzo is an experienced professional with a demonstrated track record of success in customer-facing roles. During his career, he has managed hundreds of customer relationships, and gained in-depth experience with numerous cloud-based software, infrastructure, and platform services. In his roles thus far, Nate has striven to gain technical skills that he could one day put toward a new career trajectory in Data Science and Machine Learning. To culminate that process, Nate recently completed Metis' Data Science and Machine Learning bootcamp. Now, his aim is to secure a role where he can make use of his prior experience, work as part of a great team, and continue to learn and grow in his new career.
Nate
DiRenzo, Data Science & Machine Learning
DDoS Mitigation Solution
Classification / Big Data/Data Engineering / Anomaly Detection
Mitigating DDoS attacks and ensuring service availability using Classification models.

Pramila Chaudhary, Data Science

Pramila Chaudhary is a graduate with a Masters degree in Computer Science from Nova Southeastern University and Bachelors in Electronics and Communication from NIT Puducherry India, and has a great interest in new technologies. During her master's courses in "data mining" and "data visualization" she worked on large datasets and developed interest in pursuing the data science field and joined Metis Data Science program in order to expand her knowledge on how data can shape new products. She has worked on different projects and she enjoys cleaning data, data modeling, and providing great data science solutions through the results.
Pramila
Chaudhary, Data Science
Classifying Poisonous Mushrooms: Pharmaceutical Company
Regression / Classification
Built a model using supervised learning and classification algorithms to determine whether a mushroom is poisonous or not for a pharmaceutical company and trained the model based on a dataset of 61,000 different mushroom physical characteristics and thereby improve the recall and f-beta by 24%.

Ryan Solava, Data Science & Machine Learning

Ryan is a former professor with a PhD in math and computer science. He transitioned to data science because he enjoys the challenge of solving real problems with data driven solutions. He has been coding in Python for over 10 years, and has particular interest in natural language processing, machine learning, and deep learning. Outside of work, Ryan enjoys board games, baking bread, and used book shopping.
Ryan
Solava, Data Science & Machine Learning
Can Someone Find Me a Nice Latte?
Natural Language Processing / Big Data/Data Engineering / Recommender Systems
This project leverages natural language processing tools to create recommendations to coffee shops. An interactive web application was built in Streamlit, which implements the recommendation system. Coffee shop reviews were processed with tokenization, lemming, and CountVectorizer. Topic modeling with several dimensionality reduction algorithms (LDA, LSA, and NMF) were compared. Cosine similarity of topic vectors generate the recommendations.

Saramoira Shields, Data Science & Machine Learning

Saramoira is an exceptional analyst and communicator, capable of identifying and relaying key relationships in massive datasets. Most recently, she worked as a Robotics Engineer at an early stage startup. Prior to that, she was the Assistant Director of NASA’s New York Space Grant Consortium, where she managed a multimillion dollar STEM engagement and research program with 25 member institutions. Saramoira holds a bachelor’s degree in mathematics from Cornell University, where she studied topology, differential geometry, and manifold theory. Her strong mathematical background and extensive experience in science communication, government relations, and technical project management led her to pursue a career in data science. She is particularly passionate about leveraging big data to address social problems, such as issues related to equality, democratizing access to scientific data, and public engagement/education.
Saramoira
Shields, Data Science & Machine Learning
Hurricane Satellite Image Browser
Cloud Computing (AWS/Google Cloud) / Big Data/Data Engineering / Tableau/Dashboards
The Hurricane Satellite Imagery Browser is a cloud-based app that allows users to view, process, and download hurricane data and imagery from the GOES-R geostationary weather satellites.