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.

Share your open roles with our grads.
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SPRING 2022

Chloe Bergsma-Safar, Data Science & Machine Learning

Chloe holds a BA in Spanish from the University of Michigan and a Master of Social Science from UCLA. Prior to joining Metis, she worked in various fields, including legal services, market research and education. At the beginning of the COVID-19 pandemic, she decided to finally learn Python after discovering an affinity for R - and coding in general - in grad school. This led her to join the data team of a volunteer-run tutoring organization and, eventually, to quit her job to immerse herself in Metis’ live online Data Science and Machine Learning bootcamp.
Chloe
Bergsma-Safar, Data Science & Machine Learning
Rail or Road? Comparing the carbon footprint of passenger train versus car travel
Big Data/Data Engineering
Created a data engineering pipeline that uses two APIs to calculate distances and emissions for traveling via train versus car between cities with Amtrak stations, stores data in a cloud-based MongoDB database, and deploys results via an interactive web application created with Streamlit.

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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Emil Michael Bernardo, Data Science Engineering

Mike Bernardo is a data scientist, project manager and technical specialist. Previously, he served as an Interactive Marketing Manager at Information Builders within the Global Marketing Team where he managed marketing projects and provided technical and analytical support to senior management. Outside of work, he is a musician and portrait photographer. Mike has a degree in Interdisciplinary Studies with a focus on communications, psychology and philosophy which helps him bridge the gap between marketing magic and data science.
Emil Michael
Bernardo, Data Science Engineering
Building a Data Pipeline for a ML-enabled Web App
Regression / Cloud Computing (AWS/Google Cloud) / Big Data/Data Engineering
This project is an automated data pipeline from web scraping (BS4 and Selenium) , predictive model (Linear Regression) and AWS and uses Machine Learning to predict the value of a used guitar via a Streamlit front-end.

Kate Reiss, Data Science & Machine Learning

Kate is a self-driven data scientist, strong communicator, and fast learner. Prior to Metis, Kate was a 911 Emergency Medical Technician in Los Angeles, where she quickly solved life-threatening problems in emergency situations. She holds a B.S. in Human Biology and Society from UCLA. Her technical curiosity and strong mathematical background led her to study machine learning and data science. Kate is particularly excited about natural language processing, computer vision, and building ethical and impactful machine learning products.
Kate
Reiss, Data Science & Machine Learning
Detecting Bias in Language Models: Clustering Word Embeddings from Wikipedia and Twitter Corpora
Natural Language Processing / Clustering / Big Data/Data Engineering
Many large language models used today produce biased language. This project explores how an initial text dataset may influence bias in language models by comparing word embeddings trained on Wikipedia text and Twitter text.

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%.

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.

Zoe Liao, Data Science & Engineering

Zoe is a licensed pharmacist making the transition into data science/analytics. Her interest in data started when she was a student at Northeastern University, where she took an elective class in data science and started to work as a research assistant in a clinical informatics team with more technical colleagues. She also noticed that despite all of the data collected on patients, there were still inefficiencies in healthcare workflows (at a cost to patients) that could potentially be mitigated with strategic and ethical data use. Zoe heard about the work data scientists could do and wanted to improve her own data and problem-solving skills, which led her to Metis.
Zoe
Liao, Data Science & Engineering
Covid-19 Vaccine Adverse Events Dashboard
Big Data/Data Engineering / Tableau/Dashboards / SQL
Built a data storage and processing pipeline for an interactive dashboard to visualize summary information for Covid-19 vaccines' adverse effects. The self-reported adverse event data was from the Vaccine Adverse Event Reporting System (VAERS).

WINTER 2022

Amir Khoeilar, Data Science & Machine Learning

Amir graduated from UCLA with a BS in Engineering and obtained an MBA from Mahan Business School. Having an eye for technology and professional experience by administering two startups, he decided to accelerate his career in Data Science and Analytics by obtaining a certification from Metis. Amir enjoys spending time collaborating with others. He is also passionate about cooking, fitness, outdoor activities, and environmental sustainability.
Amir
Khoeilar, Data Science & Machine Learning
Classification of "Star Clients" for GetMoney.com
Regression / Natural Language Processing / Classification
Recognizing profit making clients for eCommerce businesses is a valuable advantage. By implementing classification models for the data provided by GetMoney.com, "Star Clients" features were identified. The results will aid the marketing team to allocate the correct marketing funds, and optimizing their search for the right clients.

Atrin Sarmadi, Data Science & Machine Learning

Atrin holds an MSc in Mechanical Engineering from the University of California, Berkeley. While working at several AI and manufacturing startups, he was inspired by machine learning and data science’s ability to enrich and enhance existing industries as well as to generate completely new, disruptive features altogether. Joining this bootcamp was sparked by this interest, and he is excited to play a role in these modern technological evolutions.
Atrin
Sarmadi, Data Science & Machine Learning
SF Parking Meter Recommendation System
Big Data/Data Engineering / SQL
Built an end-to-end recommender system for SF parking meters based on user address and time inputs, Geocoding using APIs, stored and processed data using SQL and Python Pandas, created a web application using Streamlit for user interface

Chien Yuan Chang, Data Science & Machine Learning

Chien Yuan loves to find solutions and make things happen and believes that data science has many potentials to achieve these goals. He decided to come back to data science career after dedicating 8 years to social service and community work ; with a non-profit organization empowering older adults in the community to have a better and healthier life in Seattle. Chien Yuan holds bachelor's degree in Public Health and master's degree in Health Policy and Management from the National Taiwan University. He had worked as a marketing researcher in the pharmaceutical industry about one year before immigrating to the United States. Chien Yuan is currently looking for a team where he can make a positive impact and grow together with his expanding data science skills and tools.
Chien Yuan
Chang, Data Science & Machine Learning
YouTube Video Information Evaluation Web App
Cloud Computing (AWS/Google Cloud) / Big Data/Data Engineering / Neural Networks
Web app with neural network model deployed on streamlit is designed for YouTube creators to optimize titles, descriptions, tags and thumbnails. Automatic data ingestion and storage were set up on cloud.

Cindy Su, Data Science & Machine Learning

With a pivot into full-time data science, Cindy holds an advanced degree in Information Security and Accountancy. Prior to joining Metis, she has years of experience working as a test engineer for telecom system and MES system. She has strong cross-disciplinary collaborative experience in planning and designing technical solutions for business needs. Cindy has advanced proficiency in multiple programming languages and is passionate about data exploring, analytics, and problem-solving.
Cindy
Su, Data Science & Machine Learning
Identify Bankruptcy risk Through Classification
Big Data/Data Engineering / Regression / Classification
Built a classification model through feature engineering and model tuning to predict company bankruptcy risk or financial distress for bank creditors or investors.

Edward Kerr, Data Analytics

Before completing Metis, Edward was a medical technician at BioReference Laboratory in the immunology department. Graduated from New Jersey Institute of Technology with a bachelor’s degree in General Studies. His passion for data and the stories it can tell is the reason why he wanted to get into data analytics. With this goal in mind, he decided to attend Metis intensive bootcamp.
Edward
Kerr, Data Analytics
Heart Disease Predictor
Tableau/Dashboards
Medical cost is expensive for both patients and hospitals, if a predictive model was built and presented to patients before it gets to the point that they have to be admitted, not only will it save the hospital expenses but also most importantly the patient. Used Tableau to create visualizations that will convince hospitals that building a predictive model is worth the investment.

Evelyn Johnson ,Data Science & Machine Learning

Evelyn is a recent graduate of the University of Connecticut, where she earned an Honors Bachelor of Science in Allied Health Science. There, she wrote a thesis using data science to create a food environment index through a deep learning-based image recognition model, where she became fascinated by data. This combined with her role as lead coordinator for a UConn survey research project, Evelyn sought to expand her analytical skills and deepen her understanding of machine learning models. Evelyn's hunger for learning and her passion for optimization will propel her drive within the field of data science.
Evelyn
Johnson ,Data Science & Machine Learning
Home: Helping or Hurting the Environment? Classifying Energy Star Certification in NYC Apartment Buildings
Classification
House Hunting for an Environmentalist: Classifying Energy Star Certification in NYC Apartment Buildings

Ignasi Sols Balcells, Data Science & Machine Learning

Ignasi holds a Ph.D. in Cognitive Neuroscience, an M.Sc. in Neuroscience, and a B.Sc. in Biochemistry and has been coding for eight years, tackling interesting questions about human memory. Curiosity is his main driver and enjoys problem-solving, finding better and faster solutions, and making useful visualizations. Before joining Metis, Ignasi was a postdoc at NYU and Columbia University. Ignasi's broad background, bridging across different fields, has enriched him with diverse perspectives and strengths.
Ignasi
Sols Balcells, Data Science & Machine Learning
Predicting Hospital Readmissions
Classification
Developed a machine learning model that predicts which diabetic patients will be readmitted to the hospital within 30 days of discharge. This could be used to improve patient outcomes and hospital finances.

John Michitsch, Data Science & Machine Learning

When the Dot-com bubble burst, John returned his laptop and put JAVA / Sybase books in storage and heading off to pursue an MBA in Finance from Indiana University. As a VP of Finance at Citi, he came to realize he was less interested in the numbers being reported than the systems and analytics behind the numbers. This led to more IT related positions and ultimately the decision to 'retool' with Metis. He is excited to apply his consulting background to new problems and work with the latest technologies.
John
Michitsch, Data Science & Machine Learning
Stock Trend App
Cloud Computing (AWS/Google Cloud) / Tableau/Dashboards / Big Data/Data Engineering
Viewed stock price trends via a Streamlit hosted app using API calls to an Oracle Cloud Autonomous Database with time series forecasts obtained via Facebook Prophet module

Matt Ryan, Data Science & Machine Learning

Matt Ryan is an aspiring data scientist with an undergraduate background in mathematics and physics. After working in business analytics for an agricultural engineering and manufacturing company for 3 years, Matt left to pursue data science training and completed the Metis Data Science bootcamp in December 2021. In his spare time, Matt likes to road cycle around his scenic hometown of Walla Walla, play guitar, and spend time with his cat, Storme.
Matt
Ryan, Data Science & Machine Learning
Natural Language Processing and The Dune Series
Clustering / SQL / Natural Language Processing
Using natural language processing techniques, I attempted to identify and model thematic exploration across installments in the science-fiction book franchise, Dune.

Matthew Kwee, Data Science & Machine Learning

Data Scientist with STEM background, proficient in Python, SQL, and Java. Experience using regression, classification, natural language processing, and neural network models to solve business problems. Accepted to Carnegie-Mellon University but deferred enrollment until 2023. Interested in pursuing a full time career in Data Science.
Matthew
Kwee, Data Science & Machine Learning
Classifying ASL Alphabet Hand Signs
Classifying ASL Alphabet Hand Signs / Classification / Neural Networks
Classified ASL hand signs using a Multilayer Perceptron neural network and Google's MediaPipe hand pose detection module.

Michael Kamel , Data Science & Machine Learning

Michael Kamel is a Data Science enthusiast with 2 years of experience as a Business Development Analyst in the Aviation and Hospitality Industry. During this time, he learned how to use data analytics to enhance the B2B sales process. Prior to this, Michael graduated from Georgetown University with a double major in Finance and Management and a minor in Entrepreneurship. His passion during school was innovation and problem solving, and his goal to start his own company down the road. Michael hopes to continue to bolster his data science skills and pair them with his previous sales experience. His current career goals can be described by this quote from Naval Ravikant: “Learn to sell, learn to build, if you can do both, you will be unstoppable.”
Michael
Kamel , Data Science & Machine Learning
Creating Human-NFT Hybrids Using Neural Style Transfer Models
Neural Networks
The goal of this project was to utilize a Neural Style Transfer Model to fuse a person's face with the art NFT of their choosing.

Nicholas Bronson, Data Science & Machine Learning

Nick is a problem-solver who enjoys utilizing data to inform critical decision-making by answering complex questions. Prior to joining Metis, Nick worked as a researcher at an investment research firm, servicing clients at top hedge funds, private equities, and credit firms, and assisting them in understanding the intricacies of industries and companies they invested in. At Metis, Nick worked to develop his data analytics and machine learning competencies. He applied these techniques to real-world topics like understanding lumber price trends, predicting employee attrition, and analyzing sentiments from music reviews. He continues to seek projects that present fascinating challenges with the potential for impactful and transformative results for both individuals and organizations.
Nicholas
Bronson, Data Science & Machine Learning
Real-Time Data Pipeline & Dashboard for US Earthquake Analytics
SQL / Cloud Computing (AWS/Google Cloud / Big Data/Data Engineering
A data pipeline and a Streamlit app for tracking and analyzing earthquake trends between 2010 and the present. Analysis of the December 2021 earthquake surge off the coast of Oregon using this app.

Nick Kim , Data Science & Machine Learning

Nick holds a BSc in Finance and International Business from New York University and an MSc in Data Mining and Predictive Analytics from St. John's University. Prior to joining Metis, he was a corporate bond originator at UniCredit Bank AG in New York, helping American corporations understand the market dynamics and raise funding in Euro and US Dollar. His interest in weaving comprehensible narrative and client advice from financial data and fascination of machine learning's analytical abilities led him to pursue a full-time career in data science. 
Nick
Kim , Data Science & Machine Learning
Catching bad actors on Ethereum blockchain
Classification / Regression
Detecting fraud perpetrators on blockchain by applying classification techniques on Ethereum addresses and the summaries of their activity, in an effort to support everyday users managing their counterpart's risk better

Paul Troy, Data Science & Machine Learning

Paul began his career in commercial real estate as an investment analyst and project manager, however, his analytical mind and desire to always understand “why” uncovered his love for data. To accelerate his career transition into data science, he recently completed Metis’s full-time data science bootcamp. Paul is fascinated with data’s universal relevance across business and enjoys engineering impactful solutions for complex problems.
Paul
Troy, Data Science & Machine Learning
Ski Jacket Reviews: NLP Analysis
Natural Language Processing
Scraped customer ski jacket reviews from Evo.com. Applied topic modeling, part of speech tagging and sentiment analysis techniques to identify customer jacket design preferences.

Samuel Robbins, Data Science & Machine Learning

Sam is a geologist turn data scientist who recently graduated with a Masters in Geology from The University of Texas at Austin. While pursuing his degree, he completed several field seasons in Egypt, Russia, and Morocco and wrote two papers in pursuit of his research. While he enjoyed the research process–particularly visualizing all of the data he collected–he wants to transition into a field that is capable of having more tangible impacts on society. To that end, with so much data out there on any subject you can imagine, Sam views data science as an incredible tool to address a variety of pressing issues facing the world. As they say at UT, “What starts here changes the world”–Hook ‘em!
Samuel
Robbins, Data Science & Machine Learning
Crafting a Winning Message
Natural Language Processing / Regression
Used Unsupervised NLP techniques to analyze the tweets of gubernatorial candidates and built a supervised regression model to predict tweet engagement. Proposed a new workflow for message refinement applicable to campaigns and businesses.

Calvin Yu, Data Science

TBD
Calvin
Yu, Data Science
Song recommendation system
Clustering / Natural Language Processing / Recommender Systems
Built a song recommendation system based on the contents of lyrics
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FALL 2021

Alex Stake, Data Science

With a love for problem-solving and a BA in archaeology, it was the examination of data and the creation of narratives around the insights from the data that drew Alex to data science. His technical experience comes from formal collegiate training and self-instruction. He is known for providing solutions to a variety of technical issues of moderate scope and complexity.
Alex
Stake, Data Science
Predicting League of Legends Wins
Regression / Classification
Predicting winners in League of Legends within the first 15 minutes using a logistic regression model and identifying the features with the highest increase in log odds.

Cordelia Aitkin, Data Science

Cordelia’s love of problem solving and data mining lead her from the world of the social sciences to data science. Prior to joining Metis, she was a teaching professor at Rutgers (NJ) University where she both taught scientific psychology to undergraduate students, and managed graduate students teaching psychology lab classes. Cordelia has a BA in Mathematics from Williams College, and an MS and PhD in psychology from Rutgers University.
Cordelia
Aitkin, Data Science
Marketing to Banking Customers - Classification
Clustering / Classification / Regression
Which bank customers are most likely to buy a new product, based on results from a previous campaign? A Random Forest Classifier allowed us to determine which customers might be included in the new campaign.

John Lassetter, Data Science & Engineering

John is a highly curious scientist who loves to solve new and challenging problems. While getting a bachelor’s and then a master’s degree in physics, he came to realize that the skills he loved to use and grow the most were those pertaining to analyzing and modeling data. This led John to pursue a career in Data Science. To prepare for this career switch, he dove deep into literature on machine learning theory and best practices, studied for and passed the TensorFlow developer certification, and completed an intensive 10 week data science and engineering bootcamp.
John
Lassetter, Data Science & Engineering
Solar Power Production Predictor for the Consumer
Big Data / Data Engineering / SQL
The solar power production predictor is a web app that provides users with an estimate of how much money they can save on electricity by installing solar panels where they live.

Joseph Brazzale, Data Analytics

While earning his BA in Economics, Joey discovered his love for data analytics and the power of simple, clear data visualization. Both fields heavily rely on an interdisciplinary approach and an analytical mindset. Prior to joining Metis, Joey was in a sales role which taught him the importance and power of understanding his customers. His passion for storytelling, and aptitude for problem solving drove him to pursue a career in data analytics.
Joseph
Brazzale, Data Analytics
What Makes a Hit?
SQL / Big Data/Data Engineering / Regression
Built a linear regression model to predict which inputs (genre, budget, release date) have the greatest impact on domestic revenue.
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Maya Remington, Data Science & Machine Learning

Maya is a physician turned data scientist with a clinical background in Women's Health, Obstetrics & Gynecology. She holds a B.S in Biochemistry with Highest Honors from UCLA and a M.D. from UC San Diego School of Medicine. Prior to coming to Metis, she worked for an e-learning company, where she wrote medical content and managed other medical writers. Her love of problem-solving, statistical thinking, and coding led her to pursue a career in data science.
Maya
Remington, Data Science & Machine Learning
Turning down the jab: factors associated with low COVID vaccination rates in the US
Classification
Used linear regression to determine the political and socioeconomic factors that best explain low COVID-19 vaccination rates at the US county level. The primary dataset was obtained by web scraping the CDC website. Regression coefficients were used to identify the most highly correlated features.
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Melissa Cooper, Data Science & Machine Learning

After taking a hiatus from engineering to raise a family, Melissa’s data science path builds upon her hardware engineering background and incorporates machine learning through an intensive and immersive data science bootcamp. She is a curious, knowledge seeking machine learning engineer, dedicated to understanding data and applying flexible approaches to modeling and algorithms that empower solutions. Melissa earned her MS in Electrical and Computer Engineering from Carnegie Mellon University.
Melissa
Cooper, Data Science & Machine Learning
Eco-Acoustic Monitoring of Endangered Species
Classification / Big Data/Data Engineering / Neural Networks
Rare species detection in dense ecosystems is central to climate change and conservation monitoring. CNNs enable real-time processing to predict bird and frog species by converting the audio to Mel spectrogram images within a deep learning pipeline.
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Milad Afrasiabi, Data Science

Thrilled by the elegance and inherent beauty of some of the Machine Learning algorithms, Milad joined Metis in order to expand and polish his knowledge of data science. He received his PhD in the field of Neuroscience from Rutgers University which helped him develop critical thinking and a strong problem solving skills. He seeks to combine these abilities with his data science skill set, to tackle and solve real-world problems.
Milad
Afrasiabi, Data Science
Identifying the Flake!
Classification / Big Data/Data Engineering / Regression
Built a classification model to identify hotel bookings with the highest chance of being cancelled.

Mishka Asli, Data Science

Mishka is a self-driven, positive, and curious Data Scientist with strong problem solving skills. She graduated from UC Santa Barbara with a BS in Financial Mathematics and Statistics and a minor in French. She then spent several years working in logistics and demand planning in the food and beverage industry. Because of her constant curiosity to learn and passion for analytics, Mishka decided to join Metis. She enjoys data cleaning, data modeling and providing easily interpretable and actionable results to other departments in a company. When Mishka is not learning new Machine Learning algorithms, she enjoys spending time outdoors and being with friends and family.
Mishka
Asli, Data Science
Minimize Loan Default Through Classification
Regression / Classification / Tableau/Dashboards
Designed a classification model through feature engineering and tuning to help banks determine whether a loan application should be approved or denied. This can aid banks in efficiently categorizing loan applicants to maximize profits.

Nick Pondok, Data Science & Engineering

Prior to Metis, Nick worked as a Team Lead in the Operations department for a FinTech start-up that focused on providing retirement plans to small to medium sized businesses. In this role Nick was able to work closely with software engineers and data analysts to help problem solve issues with the product. Through this collaboration Nick developed an interest in data and decided to take on the challenge of switching career paths. With a knack for data storytelling and a passion for helping others, Nick hopes to have a meaningful impact in his next role.
Nick
Pondok, Data Science & Engineering
Natural Language Processing with Movie Reviews
Natural Language Processing
Generated a topic model surrounding audience reviews for Marvel's movie Shang-Chi in order to see which topics were being discussed for both positive and negative reviews.
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Zhi Wen Huang, Data Science & Engineering

Zhi has a strong knowledge in computer science fundamentals after graduating from University at Buffalo with bachelors and masters in computer science. His strong programming and problem solving skills highlight his talents in the technology industry. Zhi also develop sophistication with the experience as a retail seller, teacher assistant, and other service related experience. He has an optimistic mindset, takes feedback well, and is always willing to learn from others. His inquisitive mind makes him a strong data scientist by asking meaningful questions when solving problems. Coming from a computer science background, his goal is to bring his prior skills to the data scientist world and keep exploring, solving, and telling more stories with others.
Zhi Wen
Huang, Data Science & Engineering
Music Genres Classification
Classification / Regression
Deployed various classification models after pre-processing music sounds of 10 different genres into 35 different features in order to test the best classification model and help applications with classifying.

SUMMER 2021

Aiman Chughtai, Data Science

Aiman graduated with a B.S in Biology from Macaulay Honors College at CUNY. Prior to Metis, she worked as a researcher in a neuroscience lab, analyzing the results of multiple rehabilitative therapies on skilled motor performance in mice with bilateral and unilateral spinal cord injuries. With an initial interest in pursuing a career as a physician, many of Aiman’s prior roles were intimately involved in recommending workflow optimizations at hospitals, effectively tackling the problem of long patient wait times and overworked doctors at once. It was here that Aiman discovered a passion for using data analytics and machine learning in order to drive meaningful change in real world situations.
Aiman
Chughtai, Data Science
Need Car Insurance? - Using Classification Algorithms to Predict Cross-Buying
Classification / Regression
In order to predict whether a health insurance policy-owner would be interested in cross-buying car insurance from their insurance company, multiple classification algorithms, including Sklearn’s Logistic Regression, Decision Tree, Random Forest, XGBoost models were leveraged.
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Andrew Wong, Data Science & Engineering

Born and raised in the San Francisco Bay Area, Andrew graduated from Santa Clara University with a BS in Psychology in 2016. Andrew has over 5 years of experience working in tech, most recently working in the FinTech industry. During his time at Metis, Andrew has developed a new interest in Data Science, Analytics, and programming and is looking forward to applying his skills into the tech industry.
Andrew
Wong, Data Science & Engineering
Natural Language Processing for Amazon Video Game Reviews
Big Data/Data Engineering / Natural Language Processing / SQL
Built interactive web app for Amazon Video Game Reviews integrating Natural Language Processing and Topic Modeling. The app visualizes the positive/negative review topics to provide insight to game developers.
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Bernard Opoku, Data Science & Engineering

Bernard started his career in Philadelphia, PA where he obtained his Bachelors degree in Computer and Information Science. While in college, he participated in various summer research ranging from computational Biology to pure machine learning. After graduation, Bernard worked in sales and hospitality; wanting to return to his roots and pursue a more technical role, Bernard thought back to his research experience in undergrad where he worked on a massive data set using machine learning. Reinspired, Bernard enrolled in Metis' Data Science & Engineering track where he enjoyed using data, computing and mathematics to positively impact everyday lives of people. In his free time, Bernard enjoys playing table tennis and soccer.
Bernard
Opoku, Data Science & Engineering
Auto FAQ Answering Machine
Recommender Systems / Big Data/Data Engineering / Natural Language Processing
Have you scrolled through a company's site to find an answer to an FAQ? Well not anymore with HDFC Bank. In this project, I used natural language Processing techniques to answer customer queries based on our FAQ database which contains the sites Frequently asked Questions and answer pairs.
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Hernan Trujillo, Data Science

Hernan is a Business Administrator with extensive experience in Market Research. Charged with building industry trends and analytic reports for past clients, Hernan's interest was sparked and his desire to work with more advanced data analytic tools and machine learning served as the catalyst for him to pursue a full-time career in data science. His versatile background provides him with a familiarity of analyzing massive amounts of data while holding a keen business perspective.
Hernan
Trujillo, Data Science
Bank Marketing Prediction
Tableau/Dashboards / Big Data/Data Engineering / Classification
Based on a Bank marketing phone calls campaign, built a classification model to predict which clients will subscribe a term deposit investments account
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Lin Mei, Data Science & Engineering

Lin Mei holds a MS in Electrical Engineering from Polytechnic University, Brooklyn NY. Prior to studying Machine Learning techniques at Metis, he spent two decades developing and trouble shooting software for telecom systems. He enjoys detective type work to gain better understanding of the inner working of many different things. The many powerful new tools provided by ML leads him to Data Science as a way to resolve important real life problems.
Lin
Mei, Data Science & Engineering
Review Filtering Using NLP
Clustering / Natural Language Processing / Big Data/Data Engineering
Using Nature Language Processing techniques, filter product reviews to a much smaller set of distinct reviews, to remove reviews too similar to each other.
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Yordanos Woldebirhan, Data Science

Yordanos has an MBA from Lincoln University, California. She believes working with data is a very powerful way of identifying and coming up with solutions for real world problems. Joining Metis was her important steps to develop the necessary skills as a data scientist and she will continue to learn more and more about the field each day.
Yordanos
Woldebirhan, Data Science
Depression Drug Reviews with Topic Modeling
Tableau/Dashboards / Natural Language Processing
This project analyzes the issues that concern patients on depression medications using the reviews they provide about the drugs they are taking.
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