Jared is available for hire

Jared Cameron Stanley

Verified Expert in Engineering

Data Scientist and Software Developer

Location

Denver, CO, United States

Toptal Member Since

July 1, 2020

Jared has more than 10 years of experience in data science and holds an M.Sc. in applied physics. He has developed high-impact models across healthcare, energy, defense, and technology for startups and Fortune 500 companies. Jared's ability to connect business objectives to technical execution has made him a go-to resource for his clients, where he can comfortably contribute as a senior developer, manager, or fractional CTO.

Machine Learning Deep Learning Artificial Intelligence (AI)Computer Vision Robotics Python R Data Engineering Natural Language Processing (NLP)Data Analytics Data Visualization AI Design Deep Neural Networks Spark TensorFlow

Portfolio

Adinkra

Deep Learning, Machine Learning, Python, Robotics, Artificial Intelligence (AI)...

Guidehouse

SQL, Spark, R, Python, Artificial Intelligence (AI), Data Engineering...

PwC

SQL, Python, Apache Airflow, MemSQL, Analytics, Data Science, Machine Learning

Experience

Data Science - 10 years Python - 10 years Deep Learning - 8 years Artificial Intelligence (AI) - 8 years Machine Learning - 8 years Computer Vision - 6 years Data Engineering - 6 years Robotics - 5 years

Availability

Part-time

Preferred Environment

PyTorch, Pandas, Deep Learning, Python, Spark, Machine Learning, Computer Vision, Artificial Intelligence (AI), Data Science, Robotics

The most amazing...

...feeling is developing a solution for a client that directly transforms their business and the world.

Work Experience

CEO and Chief Engineer

2020 - PRESENT

Adinkra

Grew and managed a team of over 20 developers in computer vision, robotics, and data analytics. Ran nearly 30 projects as the technical lead, producing $50+ million in client value to date.
Built an autonomous UAV, complete with custom navigation, perception, control, and end-to-end simulation. Demonstrated real-world solutions for major defense companies and helped clients secure $10+ million in funding.
Created computer vision models for site monitoring using in-house data annotation, active learning, synthetic data generation, edge deployment, and ROS integration. Deployed to 1,000 sites with estimated savings in the millions/year for operators.
Developed a disease outbreak model, patient marketing optimization model, and COVID-19 distribution optimization model for a large US retailer. These models impacted over 100+ million customers and 10,000 stores daily.
Created a big data solution for wind turbines, then used this to develop a yaw alignment optimization algorithm deployed to 1000+ turbines and saves millions annually in OpEx.

Technologies: Deep Learning, Machine Learning, Python, Robotics, Artificial Intelligence (AI), Software Development, Data Science, Computer Vision

Senior Data Scientist

2019 - 2020

Guidehouse

Led a utility asset risk model development to inform over $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 an ML project for the energy grid in Hawaii 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 manage TB of streaming data. 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 and 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 by over 200%.
Created a model to predict window stock, turnover, and efficiency changes in the United States using Bayesian inference. The automated approach allowed us to decrease client costs for the project by around 40%.

Technologies: SQL, Spark, R, Python, Artificial Intelligence (AI), Data Engineering, Software Development, Computer Vision, Data Science, Deep Learning, Machine Learning

Data Engineer

2019 - 2019

PwC

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: SQL, Python, Apache Airflow, MemSQL, Analytics, Data Science, Machine Learning

Software Engineer

2018 - 2018

Payger

Delivered a block explorer REST application for real-time transaction monitoring of the Bitshares network.
Developed a blockchain payments platform on the Bitshares network to reduce transaction settlement times by over 10x compared to banks.
Designed an Elasticsearch back end and micro-service architecture using Java and AWS for data management and processing.

Technologies: Java, Amazon Web Services (AWS), Elasticsearch, Kibana, log4j, REST, Python, Machine Learning

Graduate Researcher

2016 - 2018

TUM

Developed an AI-based approach to solar material discovery and optimization. Developed an end-to-end solution for creating simulated data and iteratively improving the model. Presented and published work at several conferences.
Worked at the Walter Schottky Institute and developed custom imaging, computer vision, and software analysis solutions to create new photovoltaic systems.
Worked at the Max Planck Institute for plasma physics on optical system development for the Tokamak fusion system. Wrote custom software to automate experiment analysis and fit physics-based models to optical data.

Technologies: Data Science, Artificial Intelligence (AI), Product Development, Data Engineering, Software, Computer Vision, Deep Learning, Python, Machine Learning, Robotics

Product Development Consultant

2017 - 2017

Motius

Hosted design thinking and technology deep-dive workshops with several global engineering firms in electrification, virtual power plants, and blockchain. Develop technical roadmaps for new solutions in these spaces.
Created consumer technology products requiring custom PCBs, app development, data analysis, and CAD.
Developed internal resources and tools for growing tech startups, mainly focused on internal innovation incentives.

Technologies: Product Development, Blockchain, Renewable Energy, Electrical Engineering, Robotics, Python, Machine Learning

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 the 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, CAD, Electrical Engineering, Data Science, Machine Learning, Simulations, Computer Vision, Python, Artificial Intelligence (AI), Robotics

Experience

Machine Learning Perovskites

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 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

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

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 building all aspects of an application in a new language.

Skills

Languages

Python, R, SQL, Markdown, Java

Paradigms

Data Science, Anomaly Detection, REST

Other

Machine Learning, Consulting, Deep Learning, Computer Vision, Artificial Intelligence (AI), Robotics, Modeling, Mathematics, Research, Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Recurrent Neural Networks (RNNs), MLflow, Data Engineering, Data Analytics, Data Visualization, AI Design, Deep Neural Networks, Computer Vision Algorithms, Neural Networks, GPT, Generative Pre-trained Transformers (GPT), Statistical Modeling, Statistics, Big Data, EOS, Bayesian Statistics, Software Development, Analytics, Product Development, Software, Renewable Energy, Electrical Engineering, log4j, Simulations, Web Applications

Frameworks

Spark, RStudio Shiny, Apache Spark, Hadoop, Flask

Libraries/APIs

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

Tools

Git, Plotly, GitHub, Apache Airflow, sparklyr, Jupyter, Kibana, CAD

Platforms

Jupyter Notebook, MacOS, Windows, Visual Studio Code (VS Code), RStudio, Docker, Blockchain, Amazon Web Services (AWS)

Industry Expertise

Project Management

Storage

MongoDB, Elasticsearch, MemSQL

Education

2016 - 2018

Master of Science Degree in Applied and Engineering Physics

Technical University of Munich - Munich, Germany

2011 - 2015

Bachelor of Arts Degree in Physics, Minor Mathematics

University of Colorado, Boulder - Boulder, Colorado, USA

Certifications

FEBRUARY 2019 - PRESENT

Deep Learning Specialization

Coursera

DECEMBER 2018 - PRESENT

The Complete Node.js Course

Code With Mosh

DECEMBER 2018 - PRESENT

UC San Diego Big Data Specialization

Coursera

Collaboration That Works

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