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Darin Sleiter

Darin Sleiter

Stillwater, United States
Member since September 13, 2016
With a PhD in physics from Stanford, and a professional software developer background, Darin has the experience and skills to fulfill both data science and data engineering roles. He greatly enjoys using machine learning and statistics to help businesses take advantage of their data.
Darin is now available for hire
  • California Data Science
    Data Science, Machine Learning, Neural Networks, Python, TensorFlow, Keras
  • Youbeo, Inc.
    Python, Scikit-learn, Jupyter Notebook, Git, PostgreSQL, MongoDB, AWS
  • Freelance Work
    Python, Scikit-learn, TensorFlow, Jupyter Notebook, Git, PostgreSQL, MongoDB...
  • Data Science, 10 years
  • Statistics, 10 years
  • Machine Learning (ML), 5 years
  • Predictive Analytics, 5 years
  • Data Engineering, 4 years
  • Python, 2 years
Stillwater, United States
Preferred Environment
Ubuntu, Git, Jupyter Notebook, Sublime and PyCharm
The most amazing... analytics service I built was a predictive model for university student drop-out risk - trained using machine learning and accessible by web service.
  • Chief Data Scientist
    California Data Science
    2016 - PRESENT
    • Developed neural network models and optimization algorithms to improve the energy efficiency at data centers.
    Technologies: Data Science, Machine Learning, Neural Networks, Python, TensorFlow, Keras
  • Data Scientist
    Youbeo, Inc.
    2016 - PRESENT
    • Subcontracted on a variety of data science projects.
    • Worked with machine learning and predictive modeling.
    • Analyzed and processed Internet of Things sensor data.
    • Built analytics services deployed on AWS.
    Technologies: Python, Scikit-learn, Jupyter Notebook, Git, PostgreSQL, MongoDB, AWS
  • Data Science Consultant
    Freelance Work
    2016 - PRESENT
    • Helped small companies and startups take advantage of their data.
    • Created predictive models using machine learning.
    • Built web service-based data analytics products.
    • Analyzed Internet of Things big data.
    • Worked with natural language processing with neural networks.
    • Wrote classification and regression algorithms.
    • Did time-series forecasting.
    Technologies: Python, Scikit-learn, TensorFlow, Jupyter Notebook, Git, PostgreSQL, MongoDB, Linux, AWS
  • Senior Python Developer with Machine Learning Experience
    Bractlet (via Toptal)
    2016 - 2017
    • Developed a Python application which uses machine learning to calibrate time-intensive physics-based energy models using the fewest number of simulations as possible.
    Technologies: Python, Machine Learning, Scikit-learn, SciPy, Pandas, Numpy
  • Senior Data Scientist
    Bravi Software
    2015 - 2016
    • Used machine learning to build models predicting which university students are at risk of dropping out.
    • Designed and built composite scales to evaluate students across a number of dimensions.
    • Packaged the analytics platform inside a docker image accessible with a RESTful webapi.
    • Helped guide and teach junior members on the data science team.
    • Worked closely with the design and software teams to ensure good integration with the analytics platform.
    Technologies: Python, Scikit-learn, Jupyter Notebook, Linux, MongoDB, Docker, Weka
  • Software Developer
    Way2 Technology
    2012 - 2015
    • Built a highly parallel and asynchronous platform to collect data from energy meters across Brazil.
    • Developed the platform as a set of microservices using an actor-based design pattern.
    • Implemented drivers using a variety of communication protocols to communicate with energy meters.
    • Enforced clean code and unit testing practices to ensure quality software (working as a core member of the team).
    • Worked as a Scrum Master, to enable and facilitate my team through Agile development practices.
    Technologies: C#, Visual Studio, SQL, Asynchronous I/O, Mercurial, TeamCity, Octopus Deploy
  • Physics PhD Candidate - Researcher
    Stanford University
    2006 - 2012
    • Performed experimental and theoretical research into quantum computation using solid-state physics and quantum optics.
    • Designed and executed experiments in the laboratory and analyzed the data results.
    • Performed numerical simulations of complex quantum systems.
    • Used maximum likelihood estimation and confidence intervals to determine quantum system parameters from experimental data.
    • Built software and a dashboard to control multiple pieces of hardware and collect data.
    Technologies: MATLAB, LabVIEW, LabWindows, C++, Java, FPGA
  • Energy Model Calibration with Machine Learning (Development)

    While working on a Toptal project for Bractlet (an award-winning company focused on modeling building energy usage in order to improve energy efficiency), I developed an application which uses machine learning to calibrate physics-based energy models.

    These models are very powerful, but they take a long time to run, and contain a number of parameters which must be calibrated and are not known ahead of time. Thus the objective of the application was to automate the calibration of these parameters using as few iterations of the physics-based model as possible.

    The application I developed uses machine learning to model the parameter space and select parameter sets to use in simulations, simultaneously exploring the parameter space and minimizing the physics-based model error without human input.

  • Student Predictive Analytics Platform (Development)

    I worked with a team at Bravi to build a predictive model for a Brazilian university in order to indicate those students at risk of dropping out.

    We cleaned and extracted features from the raw university data, evaluated the performance of various machine learning algorithms and the models they produced, and incorporated the resulting models into a Docker image which is currently in use at the university. The predictions are used to focus early attention on students who are at risk of dropping out and maximize their chance of continuing their studies.

  • Predictive Model for Baseball Games (Development)

    My first experience with machine learning was at the end of my undergrad time at Princeton when a friend approached me to help him implement a model to predict the outcome of baseball games, which he had designed as part of his senior thesis.

    We used non-parametric statistics (before machine learning was a buzzword), and custom built a model to predict the probabilities of certain events occurring in a particular game. The model was a nearest neighbor's implementation using composite indices for dimensional reduction.

    Treating baseball betting as a market, we used the model to trade very successfully for two years before new laws made the market unavailable.

  • Data Collection Platform for Energy Data (Development)

    At Way2, I worked with an Agile team to build a platform to collect data from energy meters throughout Brazil and South America.

    We designed and built a scalable microservice solution which is highly parallel and asynchronous, robust for long term stability, can communicate using a variety of communication protocols, and has detailed logging.

    This platform is currently in use by CCEE, the Brazilian government agency which manages the Brazilian energy market, collecting data from tens of thousands of meters.

  • Predictive Model for Bike Sharing System (Development)

    The purpose of this repository is to show how I like to develop data science projects and what can be built in a few hours. It includes two Jupyter notebooks—the first showing exploratory analytics and discussion of the data, and the second showing the performance of predictive models built via machine learning.

    The performance of some standard machine learning algorithms are compared to that of a custom-designed model tailored to the system being modeled. The custom model results in a reduction of nearly half the residual error between the prediction and the testing data.

  • Verbalist Android App (Development)

    Verbalist was a productivity app for Android available on the Google Play App Store.

    It was a list manager with voice-to-text and semi-structured language processing which allowed users to control the app and add list items by voice. The app had thousands of users and a 4.8 star rating until we stopped development.

  • Languages
    Python, C#, SQL, JavaScript, C, C++, Java, HTML
  • Libraries/APIs
    NumPy, Sklearn, SciPy, Scikit-learn, Pandas, Matplotlib, Keras, TensorFlow
  • Tools
    Jupyter, Git, Mercurial (Hg), MATLAB, IPython, Weka, Mathematica
  • Paradigms
    Asynchronous Programming, Clean Code, Scrum, Unit Testing, Agile Software Development, Data Science, Parallel & Distributed Computing, DevOps, Continuous Delivery (CD)
  • Platforms
    Linux, Windows, Docker, Android
  • Misc
    Scientific Computing, Statistics, Data Mining, Predictive Analytics, Signal Processing, Data Engineering, Machine Learning (ML), Big Data, Physics Simulation, Natural Language Processing (NLP), RESTful Web Services, Neural Networks, Experimental Design
  • Frameworks
    Apache Spark
  • Storage
    MongoDB, MySQL, PostgreSQL
  • PhD in Physics
    Stanford University - Stanford, CA, USA
    2006 - 2012
  • Master's degree in Physics
    Stanford University - Stanford, CA, USA
    2006 - 2011
  • Certificate of Proficiency in Engineering Physics
    Princeton University - Princeton, NJ, USA
    2002 - 2006
  • Certificate of Proficiency in Applied and Computational Mathematics
    Princeton Univeristy - Princeton, NJ, USA
    2002 - 2006
  • Certificate of Proficiency in Applications of Computer Science
    Princeton Univeristy - Princeton, NJ, USA
    2002 - 2006
  • Bachelor's degree in Physics
    Princeton Univeristy - Princeton, NJ, USA
    2002 - 2006
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