Jake hAInes

Data Scientist & Engineer


I am an independent, well-rounded and multi-faceted data scientist with a focus on applications in engineering. My experience spans across a multitude of domains, including Python & SQL, music production software & audio engineering, and vehicle engineering.I design and execute prototype research pipelines that utilize the following: sensor implementation for data collection, processing and storing sensor data, experiment design, ad-hoc and post-hoc analysis using machine learning and deep learning models, visualization of results, and presenting the proof-of-concept to various teams for continuation of development. I am able to work and communicate cross-functionally across teams to design and execute broad and interdisciplinary projects.Nature is of great inspiration, and as such, I love to adventure-- both outside and inside the workplace. I have a diverse (albeit strange) range of interests, and treat colleagues with equal levels of respect. Having a creative, organized, and ambitious personality, I solve problems and create ideas using unique perspectives.I built Raspberry Pi traffic monitors and worked with the sensor data to find a workaround for a personally burdening scenario. I took what could have been a data entry project and converted it to a data transformation project, which saved the company hours of time and eased the transition to a new inventory software. I utilized my own data to create insights for myself and my well-being.Data are the tracks left behind by everything. I harness the tools to maximally leverage insight from those tracks and create a better world for myself, my community, my company, and outwards.


If you would like to contact me, please feel free to call, text, or email me. I will respond within 24 hours.

Raleigh, NC 27606
+1 908-323-3690

A Brief Analysis on Lithium sources

You can view the article associated with these visualizations here.

Lithium Production worldwide

Annual Lithium Production, Per Country

Growth in Lithium Production, Per Country

Annual Lithium production in select countries, with forecasts

Production trends in zimbabwe and portgual

For the best viewing experience, please click the fullscreen icon at the bottom right corner of the window.


>>> June 11, 2022

Paper: A Statistical Approach to Finding Your Optimal Apartment

>>> December 31, 2021

PROJECT: My 2021 Mental Health data Analysis

>>> July 27, 2021

Analysis: My Mini-Golf Game Performance Visualized with Plotly

>>> June 16, 2021

PROJECT: Pandas ETL for Inventory Management Software Migration

>>> May 7, 2021

Project: University Resident PArking Traffic Analysis

>>> APRIL 28, 2021

Analysis: Nickel and Cobalt - trends & Predictions

>>> April 14, 2021

Analysis: A Brief Analysis on Lithium Sources

>>> MARCH 19, 2021

PRoject: How I Created a System to Measure Parking Traffic

>>> April 6, 2021

Career Guide: 9 Tips for Creating Your Data Science Resume as an Undergrad

Nickel and Cobalt - Trends & Predictions


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Aug 2022 - Present
Tesla // Palo Alto, CA

Data Science Intern - Chassis Engineering

  • Manage dataflow from sensors to internal database

  • Transform data useful for analysis

  • Analysis of vehicle CAN data in Python

  • Evaluate sensor firmware for implementation

  • Develop visualizations of various part characteristics

  • Create interactive dashboards for visualizing sensor data

  • Analyze relationships between various data outputs from sensors

May 2022 - Aug 2022
Tesla // Fremont, CA

Research & Development Intern - Vehicle Technology Programs

  • Built proof-of-concept projects for future vehicle technologies

  • Tested third-party sensors and equipment in various use cases for technology implementation proof-of-concept

  • Collected, validated, transformed, and analyzed biometric, CAN, experiment, geospatial, third-party API, census, network, and IoT sensor data, using Python (pandas, numpy, plotly, geojson, folium, datashader, GPIO)

  • Designed and conducted full experiments and UX studies using empirical and statistical methods, various conditions, and various use cases

  • Built anonymous subject selection algorithm in Python (pandas, scipy, sklearn)

  • Built algorithm to aggregate data from various sensor data streams into one output dataset

  • Designed dataflow pipelines

  • Created internal database for data storage

  • Queried data from external suppliers using python REST API and moved data to Sharepoint database using python (office365)

  • Built interactive data visualization dashboards using internal API

  • Wrote documentation for projects, code, databases, and datasets

  • Technical program management in Visio and ClickUp

Jan 2022 - May 2022
Conceptualee // Raleigh, NC

Lead Data Science Intern

  • Collected data on indicators for investment-backed expandable sustainable development in Nigeria

  • Reshaped and reformatted data

  • Designed data management pipeline

  • Uploaded data to PostgreSQL database

  • Executed SQL queries on server

  • Facilitated tasks and training for team members

  • Regularly communicated and met with team members to assist them on their needs

Jan 2022 - May 2022
Varsity Tutors // Raleigh, NC

Data Analytics Tutor

  • Tutoring in data science and analytics

  • Tutoring in statistics

  • Create lesson plans according to clients' individual needs

  • Provide interactive lessons and assignments for clients

  • Provide support and motivation for clients

May 2021 - Sep 2021
Eagle Parts & PRoducts // Augusta, GA

Data Science Intern

  • Restructured tens of thousands of rows of inventory data

  • Cleaned and parsed data in Python, using libraries: pandas, regex, usaddress

  • Transformed data for use with company's new inventory management software

Jan 2021 - May 2021
University of North Carolina at Asheville // Asheville, NC

Research in Statistics

  • Comprehensive analysis of parking garage traffic on campus to predict demand for residential parking

  • Collected data via Raspberry Pi systems running a TensorFlow Lite object detection model

  • Performed ETL on generated CSVs in Visual Studio SSDT (SSIS) and Microsoft SSMS (SQL) to clean data

  • Data mining, analysis, and visualization in Tableau

  • Presented at UNC Asheville and Wake Forest University, published project to blog


Aug 2021 - May 2024
North Carolina State University // Raleigh, NC

BS, Statistics

  • Data Analytics Club at Institute for Advanced Analytics

Aug 2019 - May 2021
University of North Carolina at Asheville // Asheville, NC

Computer Science

  • GPA: 3.5

  • Minor in mathematics

  • Dean’s List: Fall 2019, Spring 2020

  • Completed undergraduate research: Spring 2021

2015 - 2019
Hunterdon Central Regional High School // Flemington, NJ


  • Marching band

  • Volunteer DJ

  • German club


  • Software: Branca, DataGrip, DataSpell, Docker, Folium, Git, GitHub, Heroku, Jupyter, Keras, Linux, Matplotlib, Microsoft Excel, Microsoft SQL Server, Numpy, OpenCV, Pandas, Plotly, PostgreSQL, PyCharm, Python, R, SQL Server Integration Services (SSIS), Tableau, TensorFlow, Transact-SQL (T-SQL), Voila

  • Languages: English (Native proficiency), German (Limited working proficiency)

Licenses & Certifications

Issued: Jul 2021
Udemy // No expiration

Python A-Z: Python for Data Science

  • Learn to program in Python at a good level

  • Learn how to code in Jupyter Notebooks

  • Learn the core principles of programming

  • Learn how to create variables

  • Learn about integer, float, logical, string and other types in Python

  • Learn how to create a while() loop and a for() loop in Python

  • Learn how to install packages in Python

  • Understand the Law of Large Numbers

Issued: Dec 2020
Udemy // No expiration

Data Science A-Z

  • Data mining in Tableau

  • Simple and multiple linear regression

  • Logistic regression

  • Building a geodemographic segmentation model

  • Assessing, analyzing, and maintaining model

  • ETL pipeline and debugging

  • Data wrangling

  • Uploading data to database using SSIS

  • SQL data structures, data conversions, joins, data cleaning, data transformation, and procs

  • Cross-departmental communication

  • Creating meaningful business insights

  • Presenting results of data science projects