Daniel Reeder -
Data Scientist and Analyst

I am a data scientist and analyst based in Portland, Oregon. As a graduate of the University of Oregon's Data Science program, I am prepared to apply my analytical skills to real-world issues. My background in Earth Science has given me a unique perspective on the importance of sustainable energy and the role that data science can play in advancing solar technology. Since graduating, I have been working with Phyton PV, a solar tech startup focused on creating innovative solutions for the transportation sector. I am passionate about using my skills to make a positive impact on the world, and I am always looking for new opportunities to learn and grow as a data scientist.

Education

University of Oregon - B.S. in Data Science

Graduated Dec 2024

3.9 GPA

Emphasis on Earth Science

Courses Taken: Foundational Data Science, Machine Learning, Principles of Data Science, Statistics, Signal Processing, Real Analysis, Linear Algebra, Computer Organization, Data Ethics, Earth Physics, Geologic Hazards, Geophysical and Environmental Sensors

Portland State University - Coursework in Computer Science

Attended Sep 2020 - Jun 2022

3.9 GPA

Courses Taken: Intro to Programming, Intro to Computer Science, Data Structures, Discrete Structures, System Programming and Architecture, Technical Writing

Churchill High School - HS and International Baccalaureate Diplomas

Graduated Jun 2020

4.0 GPA

Skills

Programming Languages:
Python, R, SQL, Java, JavaScript, C, C++

Technical Skills:
Machine Learning (Pytorch, Scikitlearn), Data Cleaning (Pandas, Numpy, Tableau), Data Visualization (Matplotlib, Seaborn, Tableau, PowerBI), Signal Processing (Python), Time Series Analysis

Other Technology:
Tableau, PowerBI, AWS Cloud

Other Skills:
Technical Writing, Leadership, Communication, Organization, Training, Presentation, Delegation

Projects

Analysis of MLB Award Winners

Personally scraped and compiled a dataset containing every season by an MLB player since 1947, using the Python library BeautifulSoup to extract data from Baseball Reference
Followed standard workflow of exploration, cleaning, inference, and prediction, using Python libraries such as Pandas, Numpy, Matplotlib, and Seaborn to analyze the data and create visualizations to communicate insights
Tested for differences between groups via distributions of sample means and confidence intervals, and used logistic regression and a random forest classifier to predict award winners based on season statistics, achieving an accuracy of nearly 90% on the test set
Used K-Fold Cross Validation for feature selection to maximize prediction accuracy

Detecting Anomalies in Weather Patterns

Used signal processing techniques to identify peaks in frequency spectra of temperature and precipitation time series data for 10 major Canadian cities, then performing detailed analysis of a single city to identify anomalous weather patterns
Used Python libraries such as Pandas, Numpy, Matplotlib, and Seaborn to clean and analyze the data and create visualizations
Used Fast Fourier Transform, Butterworth filters, and spectrograms to identify anomalies visually and outlined potential future steps to identify causes, automate the anomaly detection process, and predict future anomalies with machine learning models
Compiled and explained my findings in both a formal written report and a presentation, with the aim of communicating complex data science concepts to both technical and non-technical audiences

Analysis of Hillslope Properties and Landslide Recurrence Intervals

Investigated the relationship between various properties of hillslopes and the recurrence intervals of landslides using well-known landslide modeling equations for shear stress and strength, critical soil thickness, soil infilling, and recurrence interval
Conducted a Monte Carlo error propagation analysis to determine the sensitivity of the recurrence interval to changes in each of the input parameters, and identified which parameters had the greatest influence on the recurrence interval
Presented expected distributions of recurrence intervals based on simulated values of a variety of input parameters

Employment

Data Analyst - Phyton PV

Mar 2025 - Present

Used research skills to gather and compile datasets containing information on electric car usage, electric car charging costs, average daily driving distance, and national and global energy consumption statistics
Cleaned and organized datasets using Python libraries such as Pandas and Numpy, ensuring data quality and consistency for analysis
Created detailed and interactive data visualizations using Tableau and Python via Matplotlib to communicate insights, trends, and product value to potential investors, aiding in strategic decision-making for the company
Contributed to foundational aspects of the company, including branding, naming, wesbite design, pitch deck creation, and insight into the correct steps to take from a business, technical, and product design perspective

Valet/Bellman - The Inn at the 5th

Jan 2023 - May 2025

Manage the parking and retrieval of up to 100 vehicles per shift, ensuring a seamless experience for guests and minimizing wait times while coordinating with a team of up to 8 members.
Provide top-tier customer service by offering information about local attractions, addressing inquiries, contributing to a positive and welcoming atmosphere.
Maintain cleanliness in the work environment by proactively inspecting and organizing the valet area and ensuring the safety and security of all vehicles under my care.
Provided shuttle rides and valet service to high-profile guests, including celebrities and professional athletes, while maintaining confidentiality and professionalism.

Daniel Reeder - Data Scientist and Analyst