Jared Cameron Stanley, Data Engineering Developer in Denver, CO, United States
Jared Cameron Stanley

Data Engineering Developer in Denver, CO, United States

Member since May 22, 2020
Jared has over five years' experience as a data scientist and machine learning engineer, having led teams on several $100,000+ data science projects and developing high-impact models, e.g., that informed $5 billion+ in investments. He holds an M.Sc. in applied physics with a focus on machine learning, and several certifications in deep learning, big data, and application development. His clients have ranged from small startups to Fortune 500 companies across North America, Europe, and Africa.
Jared is now available for hire

Portfolio

Experience

Location

Denver, CO, United States

Availability

Part-time

Preferred Environment

PyTorch, Keras, Scikit-learn, NumPy, Pandas, TensorFlow, SQL, Deep Learning, R, Python

The most amazing...

...recent project I completed helped my client expand their machine learning services across Europe to multiple languages and reduce training costs upwards of 70%.

Employment

  • Senior Machine Learning Engineer, Senior Data Scientist

    2019 - PRESENT
    Self-employed
    • Developed an end-to-end REST application for machine learning model management and microservices using MLflow, including fastText, and BERT encoders.
    • Developed state-of-the-art NLP models in multiple languages for named entity recognition, sentiment analysis, topic analysis, and intent recognition using TensorFlow, Keras, and SpaCy.
    • Created an online learning process to continuously train algorithms, reduce annotation labor costs up to 30%, and integrate into a chatbot application.
    • Developed customer segmentation classifiers based on sales, orders, and customer demographics to drive revenue increases by ~20% for a retail company. Applied computer vision to detect and annotate products automatically based on produce images.
    • Worked on developing an options trading algorithm for the client to maximize profits in volatile markets and detect underpriced assets. This is an ongoing process.
    • Created predictive maintenance and wind power optimization models in Python for wind turbines. Architected a cloud solution and automated a reporting pipeline using Spark, Databricks, Azure, and SQL.
    • Developed cutting-edge real-time computer vision models (Deep Sort, YOLO, and Siamese networks) for multi-object tracking and object detection on drones. Created an automated pipeline for training, saving up new detection categories' costs to 90%.
    Technologies: TensorFlow, OpenCV, Deep Learning, Natural Language Processing (NLP), Machine Learning, Python
  • Senior Data Science Consultant

    2019 - 2020
    Guidehouse
    • Led a utility asset risk model development to inform over USD $5 billion in grid resiliency planning. This included an ETL pipeline for hundreds of files and formats from scratch, weather modeling, classifiers for imputation, and graph analysis.
    • Managed a USD $150,000 machine learning project to predict auto accidents and grid impacts for an electric utility. Collected roads, weather, utility, and traffic data. Built several classifiers with an AUC ROC of 89%. Led client technical workshops.
    • Led big data proof-of-concept using Spark, R, and Scala to process TB-sized data sets. This included a robust standard error package in Spark, dynamic time warping machine learning tools for customer segmentation, and Spark transformation pipelines.
    • Spearheaded the development of an internal weather package to process NOAA data for firm-wide projects. Created FlexDash and Shiny dashboards, as well as parameterized QC memos to visualize data and assess completeness.
    • Served as a lead modeler of the Bass diffusion model for forecasting electric vehicle (EV) adoption and EV siting analysis. Applied linear programming and optimization techniques to improve analysis times over 200%.
    • Created a model to predict window stock, turnover, and efficiency changes in the United States using Bayesian inference with Dirichlet priors. The automated approach allowed us to decrease client costs for the project by ~40%.
    Technologies: SQL, Spark, R, Python
  • Data Engineer

    2019 - 2019
    Hays Consulting
    • Architected the enterprise ETL solution to extract data from data lakes and major ERPs, process it using ephemeral MemSQL clusters, and update data warehouses. Included REST APIs, Airflow, and dynamic SQL.
    • Developed a custom QC and testing suite in Python to perform regression, integration, and unit testing. Quality checks and Type 2 tracking ensured the highest data integrity.
    • Developed process mining and outlier analysis tools including custom dashboards using D3 and Zoom.
    Technologies: JavaScript, SQL, Python, Apache Airflow
  • Software Engineer

    2018 - 2018
    Payger
    • Developed a blockchain payments platform on the BitShares network to reduce settlement times by over 1,000% compared to traditional methods.
    • Designed an Elasticsearch back end and micro-service architecture using Java and AWS for data management and processing.
    • Delivered a block explorer REST application for real-time transaction monitoring of the BitShares network.
    Technologies: Amazon Web Services (AWS), ELK (Elastic Stack), BitShares, EOS, AWS, Java
  • Professional Research Assistant

    2014 - 2016
    Laboratory for Atmospheric and Space Physics
    • Developed lunar dust and mass spectrometer models to process millions of image-charge signals for the LADEE lunar mission. Presented work at AGU 2015.
    • Developed image processing tools in IDL and Swift for dust accelerator calibration experiments.
    • Led development of the SUDA mass spectrometer lab prototype for Europa's Clipper mission. Worked across science, engineering, and simulation groups to fabricate mechanical and electrical components and construct a working device for under $10,000.
    Technologies: Swift, IDL

Experience

  • Machine Learning Perovskites (Development)
    https://github.com/jstanai/Machine-Learning-Perovskite-Properties-for-Photovoltaics

    This was my thesis work creating a novel approach to property prediction for photovoltaics. The work has been published in Advanced Theory and Simulation and featured by the Synopsys. The tool kit allows researchers and developers to easily schedule large quantum simulation jobs on a cluster and extract key results for material science applications.

  • Tidyspark (Development)
    https://github.com/danzafar/tidyspark

    I helped contribute to an open-source project "tidyspark" which provides an R interface for running Spark. This interface offers tidy functionality and syntax to the SparkR back end, allowing a cleaner and more useable method for bringing Spark into data science applications with R.

  • Movie Rental Application (Development)
    https://github.com/jstanai/Video-Rental-Application

    I developed a RESTful movie rental application in Node.js complete with user authentication, a MongoDB back end, request validation and modeling, and a testing framework. The goal was to gain experience in building all aspects of an application in a new language.

Skills

  • Languages

    Python, R, SQL, Markdown, JavaScript, Java, IDL, Swift
  • Paradigms

    Data Science
  • Other

    Machine Learning, Consulting, Modeling, Mathematics, Research, Software Development, Deep Learning, Convolutional Neural Networks, Natural Language Processing (NLP), Recurrent Neural Networks, MLflow, Data Engineering, Data Analytics, Data Visualization, Artificial Intelligence (AI), AI Design, Statistical Modeling, Computer Vision, Statistics, Big Data, AWS, EOS
  • Frameworks

    Spark, RStudio Shiny, Apache Spark, Hadoop, Flask, WebApp
  • Libraries/APIs

    TensorFlow, Keras, REST API, SpaCy, Node.js, PySpark, OpenCV, Pandas, NumPy, Scikit-learn, PyTorch
  • Tools

    Git, Plotly, GitHub, Apache Airflow, sparklyr, R Studio, Jupyter, ELK (Elastic Stack)
  • Platforms

    Jupyter Notebook, Blockchain, MacOS, Windows, Visual Studio Code, BitShares, Amazon Web Services (AWS)
  • Industry Expertise

    Project Management
  • Storage

    MongoDB, Elasticsearch

Education

  • Master of Science degree in Applied and Engineering Physics
    2016 - 2018
    Technical University of Munich - Munich, Germany
  • Bachelor of Arts degree in Physics, Minor Mathematics
    2011 - 2015
    University of Colorado, Boulder - Boulder, Colorado, USA

Certifications

  • Deep Learning Specialization
    FEBRUARY 2019 - PRESENT
    Coursera
  • The Complete Node.js Course
    DECEMBER 2018 - PRESENT
    Code With Mosh
  • UC San Diego Big Data Specialization
    DECEMBER 2018 - PRESENT
    Coursera

To view more profiles

Join Toptal
Share it with others