Ryan Farr

Data Scientist

https://www.opentalent.in/ryan-farr

Member since June 25, 2026

Key Strengths

Demonstrated interest in Data Science and Machine Learning through various personal projects.
Proficiency in Python for data science and algorithmic implementations.
Experience with core machine learning concepts like sentiment analysis, clustering, random forests, and Gaussian mixture models.
Familiarity with planning algorithms (MDP, Value Iteration, Policy Iteration) and search algorithms (DFS, BFS, UCS, A*).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are primarily academic or personal, indicating a strong drive for learning and exploration in data science and related fields. The diversity of projects, from planning algorithms to sentiment analysis, suggests an adaptable and curious mindset. However, the lack of team-based projects or professional experience makes it difficult to fully assess cultural fit in a collaborative work environment.

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. No psychometric test results or interview feedback provided.

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Projects

QIP_Project

April 9, 2026 – Present

QIP_Project — GitHub repository

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Multi-Agent-RRT

March 3, 2025 – March 3, 2025

Mutli-agent implementation of rapidly-exploring random trees connect (RRT connect) algorithm applied to high-speed police pursuits

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Footprint

February 15, 2021 – February 15, 2021

Footprint — GitHub repository

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Crypto_HW2

March 27, 2019 – March 28, 2019

Crypto_HW2 — GitHub repository

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RandomForestRegression

November 20, 2018 – November 28, 2018

Using random forests to approximate images

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SentimentAnalysis_Clustering

November 5, 2018 – November 9, 2018

Sentiment analysis, clustering, Gaussian mixture model, PCA

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PlanningWithUncertainty

November 8, 2016 – November 19, 2016

Markov Decision Process, Value Iteration and Policy Iteration implementations for planning in a probabilistic environment

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SamplingBasedPlanning

October 10, 2016 – October 14, 2016

A python solution to sampling based planning using Rapidly-Exploring Random Trees and Probabilistic Roadmaps

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Graph-Search

September 7, 2016 – September 10, 2016

My solution to CS 6370 - Motion Planning. Contains depth first search, breadth first search, uniform cost search and A* search

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Malloc-Lab

November 18, 2015 – November 23, 2015

Malloc Lab for CS 4400 - Computer Systems

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Key Strengths

Demonstrated interest in Data Science and Machine Learning through various personal projects.
Proficiency in Python for data science and algorithmic implementations.
Experience with core machine learning concepts like sentiment analysis, clustering, random forests, and Gaussian mixture models.
Familiarity with planning algorithms (MDP, Value Iteration, Policy Iteration) and search algorithms (DFS, BFS, UCS, A*).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. No psychometric test results or interview feedback provided.

Ryan Farr

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit