Data Science Consultant
2013 - PRESENTOphidian Scientific- Assisted numerous small clients with data-related work, ranging from data science and analysis, data engineering, and machine learning engineering.
- Designed and built ETL pipelines in Python, Dask, and Prefect.
- Oversaw the migrations between Google Sheets and Airtable. Airtable automation was execued in Python.
- Used operations research libraries in Python to optimize teams for the sports betting website FanDuel.
- Built a natural language processing (NLP) classifier for an article archive of a finance-based publication.
Technologies: Amazon Web Services (AWS), PostgreSQL, Keras, XGBoost, Random Forests, Spark, Database Design, Experimental Design, Clojure, Docker, Jupyter, Time Series, AWS, Pandas, SQL, Machine Learning, Natural Language Processing (NLP), Operations Research, Data Visualization, ETL, Scientific Data Analysis, Data Engineering, Data Science, PythonData Scientist & Data Architect
2021 - 2021Birch Infrastructure- Assisted with architect data infrastructure for a utility-scale renewable energy and data center company.
- Created data pipelines with Prefect, mostly stitching together Google Cloud Functions and Cloud Run jobs.
- Managed BigQuery data warehouse with dbt, made table schemas and transformations.
- Set up data infrastructure (including Prefect and dbt).
Technologies: Google Cloud Platform (GCP), BigQuery, Data Building Tool (DBT), Prefect, Python, ServerlessSenior Data Scientist
2018 - 2019The University of Colorado — Office of Data Analytics- Performed statistical analyses and modeling to support student success and helped establish practices during a restructuring of the university’s office of data analytics.
- Created and presented findings and visualizations to high-level administrators with Jupyter and Zeppelin.
- Developed a Monte Carlo simulation-based model to predict semester-by-semester student retention.
- Built a Bayesian model of re-offense after student misconduct.
- Modeled the effects of different kinds of financial aid with XGBoost.
- Created a model to predict student GPAs with scikit-learn and Keras.
- Helped establish practices during a restructuring of the university’s office of data analytics.
Technologies: Amazon Web Services (AWS), XGBoost, Random Forests, Experimental Design, Data Visualization, Time Series, AWS, SQL, Data Science, Machine Learning, Oracle Database, Zeppelin, Jupyter, Keras, PySpark, Scikit-learn, Pandas, PythonData Engineer
2017 - 2018NOMI Beauty- Designed and built the data infrastructure for a startup that made it easier to book hair-&-makeup appointments in hotel rooms.
- Architected a big data pipeline with Spark, Kafka, and Cassandra.
- Built data dashboards in Tableau for the operations team.
- Designed an ETL for survey data from Typeform's API into MySQL.
- Created reports in Jupyter notebooks with data visualizations in Python with Altair and Seaborn.
- Designed and implemented a database schema in MySQL.
- Designed and supported ETL from Couchbase to MySQL using Python.
Technologies: Amazon Web Services (AWS), Spark, Database Design, Data Visualization, SQL, Jupyter, Simulations, AWS, Cassandra, Apache Kafka, PySpark, MySQL, Pandas, PythonData Science and Blockchain Integration Consultant
2017 - 2017Tanktwo, Inc.- Architected a blockchain-based solution for managing IoT devices and the data they generate.
- Create a demo of a potential network using Hyperledger.
- Simulated a private blockchain network in action, using Python.
- Helped present a demo to the venture capitalists who were looking to invest.
- Conducted research on optimal blockchain implementation to suit business needs.
Technologies: Amazon Web Services (AWS), Jupyter, Data Visualization, Time Series, AWS, Hyperledger, Pandas, PythonData Science Consultant
2014 - 2017Hospital for Special Surgery- Worked on data science topics in a neurology lab that investigated intraoperative neurophysiological monitoring (IONM)—monitoring muscles and nerves during surgery to prevent damage.
- Reverse-engineered an undocumented file format containing biosignal data.
- Attempted to classify nerve conduction readings as indicating injury or anesthesia response using Scikit-learn.
- Visualized biosignal data with Plotly and presented findings.
- Investigated using Higuchi Fractal Dimension of nerve conduction readings taken during surgery as a means of assessing potential damage.
- Analyzed biosignal data with a Python data suite (NumPy, Pandas, and SciPy).
Technologies: Experimental Design, Data Visualization, Time Series, Data Science, Machine Learning, Scikit-learn, PyEEG, Jupyter, Plotly, SciPy, Pandas, NumPy, PythonNatural Language Processing Consultant
2015 - 2015New York City Department of Administrative Services- Scraped PDFs with Python to help digitize the back catalog for a publication, The City Record.
- Helped design a schema for entries (such as extracting addresses).
- Created data cleaning regimens to standardize entries from over a hundred city agencies reported in different formats.
- Used Python and NLTK to perform exploratory natural language processing (NLP) on a century-long corpus of publications.
- Worked to integrate a pipeline into their MS Access.
Technologies: Jupyter, Data Visualization, Data Science, Machine Learning, Python, NLTKIntegration and Development Consultant
2013 - 2014Broadband Technologies Group- Provided computer vision-based assistance for digitizing video archives.
- Used OpenCV and Python to tag damaged video areas.
- Implemented Python to automatically fix certain types of damaged videoes.
- Helped architect an Android application to deliver simultaneous subtitles for live performances.
- Prepared presentations with Jupyter.
Technologies: Jupyter, Data Visualization, OpenCV, PythonResearch Assistant
2008 - 2013Hunter College- Designed and validated a novel psychometric scale.
- Analyzed survey data in SPSS.
- Presented findings at research conferences.
- Maintained relationships with the lab after graduation, eventually moving from data analysis to Python.
- Worked on the publication of older data.
Technologies: Experimental Design, Data Visualization, Data Science, SciPy, Python, SPSSSummer Research Assistant
2009 - 2010Yale School of Medicine- Designed and piloted a small study investigating psychopathic traits and behavior during an ultimatum game.
- Analyzed GSR data.
- Ran research participants through computer-based tasks in a presentation and DMDX.
- Analyzed data from surveys and computer-based tasks.
- Built and maintained a database of participants.
Technologies: Experimental Design, Data Visualization, Data Science, DMDX, SPSS
Matthew Alhonte
Data Scientist and Developer in New York, NY, United States
Member since June 20, 2018
Matt has officially worked as a Python-based data scientist for the past six years; however, he's spent the last ten at the intersection of stats and programming (before the term data scientist had caught on). He combines strong technical skills with a rigorous background in experiment design and statistical inference. More recently, he's been focusing on machine learning, including some natural language processing and computer vision.
Matthew is now available for hire
Portfolio
- Ophidian ScientificAmazon Web Services (AWS), PostgreSQL, Keras, XGBoost, Random Forests, Spark...
- Birch InfrastructureGoogle Cloud Platform (GCP), BigQuery, Data Building Tool (DBT), Prefect...
- The University of Colorado — Office of Data AnalyticsAmazon Web Services (AWS), XGBoost, Random Forests, Experimental Design...
Experience
- Statistics 11 years
- Data Visualization 11 years
- Python 6 years
- Pandas 5 years
- SQL 5 years
- Machine Learning 5 years
- Functional Programming 4 years
- Scikit-learn 3 years
Availability
Part-time
Preferred Environment
PyCharm, Git, Spacemacs, VS Code, Jupyter
The most amazing...
...thing I've done is to reverse-engineer an undocumented file format containing electrophysiology readings.
Employment
Experience
- Spring 2018 Complexity Challengehttps://github.com/mattalhonte/sfi-challenge
My entry in the Spring 2018 Complexity Challenge by the Santa Fe Institute.
- Graph Theory Noteshttps://github.com/mattalhonte/SFI-Algorithmic-Information-Theory/blob/master/2.%20Graph%20Theory%20(n-Cubes).ipynb
This is some code that I wrote to help me to understand the graph theory section of an online course on algorithmic information theory.
- Binary Grid Searchhttps://hackersandslackers.com/tuning-machine-learning-hyperparameters-with-binary-search/
Here I am experimenting with using a binary search to tune hyperparameters for machine learning models in Scikit-learn.
- Recasting Low-cardinality Columns as Categoricalshttps://hackersandslackers.com/recasting-low-cardinality-columns-as-categoricals-2
A short tutorial on saving memory in Pandas by using categorical variables. It includes a code snippet to take a data frame and recast the low-cardinality columns as categoricals.
- Removing Duplicate Columns in Pandashttps://hackersandslackers.com/remove-duplicate-columns-in-pandas
A short tutorial on finding and removing duplicate columns in Pandas.
- Downcast Numerical Data Types with Pandashttps://hackersandslackers.com/downcast-numerical-columns-python-pandas/
A short tutorial on saving memory by downcasting Pandas columns into the smallest possible numerical representation.
- Sentiment Analysis With AWS SageMakerhttps://github.com/mattalhonte/sagemaker-deployment/tree/master/Project
Classifying movie reviews as positive or negative, using SageMaker's version of XGBoost.
- Epilepsy Classifierhttps://github.com/mattalhonte/epilepsy-classifier
A capstone project for Udacity's machine learning engineer nanodegree.
- Python to Rust
A short walkthrough of training a machine learning model in Python, exporting a model artifact, and serving predictions in Rust. It was accepted as official documentation for a relevant Rust crate called "tract."
- Splitting Columns With Pandashttps://hackersandslackers.com/splitting-columns-with-pandas/
I wrote a tutorial on splitting up Pandas columns with nested data.
Skills
Languages
Python 3, Python, SQL, Snowflake, Clojure, RustLibraries/APIs
Pandas, Scikit-learn, TensorFlow Deep Learning Library (TFLearn), XGBoost, NumPy, Keras, Dask, SciPy, OpenCV, NLTK, PySpark, TensorFlowTools
DataViz, Jupyter, VS Code, Spacemacs, PyCharm, SPSS, Plotly, DMDX, Git, Amazon SageMaker, BigQueryParadigms
Data Science, Database Design, Agile, Functional Programming, ETLPlatforms
Jupyter Notebook, Amazon Web Services (AWS), Docker, Hyperledger, Oracle Database, Linux, Zeppelin, Apache Kafka, Google Cloud Platform (GCP)Other
Data, Statistical Data Analysis, Exploratory Data Analysis, Unstructured Data Analysis, Complex Data Analysis, Statistical Methods, Statistical Modeling, Statistical Forecasting, Statistical Analysis, Statistical Significance, Random Forests, Random Forest Regression, Experimental Design, Time Series, Machine Learning, Predictive Modeling, Data Visualization, Data Analysis, Data Analytics, Statistics, AWS, Computational Statistics, Bayesian Statistics, Statistical Programming, Amazon Machine Learning, Tf-idf, Convolutional Neural Networks, Analysis of Variance (ANOVA), Dashboards, Analytical Dashboards, Data Building Tool (DBT), Deep Learning, Natural Language Processing (NLP), Mathematical Modeling, Data Engineering, Operations Research, Simulations, PyEEG, Scientific Data Analysis, Prefect, ServerlessStorage
Databases, NoSQL, Cassandra, PostgreSQL, MySQLFrameworks
Spark
Education
- Bachelor of Arts Degree in Psychology2006 - 2012Hunter College - New York City, NY, USA
Certifications
- Machine Learning Engineer NanodegreeJANUARY 2020 - PRESENTUdacity
