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Darin Erat Sleiter

Darin Erat Sleiter

Stillwater, MN, 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
  • Data Science, 11 years
  • Statistics, 11 years
  • Machine Learning, 6 years
  • Predictive Analytics, 6 years
  • Data Engineering, 5 years
  • Python, 3 years
Stillwater, MN, United States
Preferred Environment
Ubuntu, Git, Jupyter Notebook, Sublime and PyCharm
The most amazing...
...product I've built combines machine learning with physics-based modeling to optimize energy usage within a data center.
  • Co-founder and Chief Data Scientist
    2016 - 2018
    California Data Science
    • Hired and lead a team of data scientists building AI products for the data center industry.
    • Built product based on machine learning, simulation, and optimization which optimizes energy consumption by the cooling system of data centers.
    • Implemented predictive maintenance tools using machine learning.
    • Contributed to every part of the process of creating, operating, and growing a small startup.
    Technologies: Data Science, Machine Learning, Neural Networks, Python, TensorFlow, Keras
  • Freelance Senior Python Developer with Machine Learning Experience
    2016 - 2017
    Bractlet (via Toptal)
    • 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
  • Data Scientist
    2016 - 2017
    Youbeo, Inc.
    • 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
  • Freelance Data Science Consultant
    2016 - 2017
    Freelance Work
    • Helped small companies and startups take advantage of their data.
    • Created predictive models using machine learning.
    • Built web service-based data analytics products.
    • Analyzed IoT big data.
    • Worked with natural language processing with neural networks.
    • Wrote classification and regression algorithms.
    • Implemented time-series forecasting.
    Technologies: Python, Scikit-learn, TensorFlow, Jupyter Notebook, Git, PostgreSQL, MongoDB, Linux, AWS
  • Senior Data Scientist
    2015 - 2016
    Bravi Software
    • 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 web API.
    • 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
    2012 - 2015
    Way2 Technology
    • 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 and Researcher
    2006 - 2012
    Stanford University
    • 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.

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