Data Scientist

About Patch Monkey 

Patch Monkey is a software development consultancy dedicated to building human-centered software and team-focused solutions. We care about business outcomes and the people achieving them, and we design software that supports both. 

Patch Monkeyers work remotely, but Patch Monkey is based in Oklahoma City. While we encourage all qualified candidates based within the US to apply, preference will be given to candidates located in or near the Oklahoma City area. 

About the Role 

Patch Monkey is looking for a Data Scientist to design, develop, implement, and maintain analytic solutions that transform data into actionable insights for a client in the agriculture sector. This project is driving innovation within the industry, and you’ll have the opportunity to work with several years of real-world data at a scale unique to agriculture and cattle operations. 

In this role, you will build and deploy machine learning, data mining, and statistical models that support decision-making and improve operational efficiency. You’ll partner closely with engineers and stakeholders to bring analytics into production environments. 

What You’ll Do 

  • Design and develop predictive and descriptive models that drive business decisions 

  • Apply statistical and machine learning approaches across structured and real-world operational data 

  • Analyze trends and patterns over time (including time series and longitudinal data) 

  • Collaborate with software engineers to deploy and maintain production analytics 

  • Communicate findings clearly to both technical and non-technical stakeholders 

 

Required Skills & Capabilities 

  • Strong quantitative, analytical, and data interpretation skills with a solid foundation in math, probability, and statistics 

  • Hands-on experience with supervised learning (classification, regression) and unsupervised learning (clustering, dimensionality reduction) 

  • Familiarity with a variety of modeling techniques, including (but not limited to): 

  • Generalized linear models (GLMs) and linear regression 

  • Logistic and multinomial regression 

  • Time series analysis 

  • Proficiency in Python and SQL 

  • Experience with ML libraries and frameworks such as scikit-learn (and/or TensorFlow) 

  • Exposure to optimization methods such as linear programming 

  • Knowledge of database modeling standards and best practices 

  • Ability to work independently with strong critical thinking and ownership 

Preferred Qualifications 

  • Experience working with agriculture and/or cattle industry data 

  • Knowledge of Ruby and SAS 

Work Authorization 

Applicants must be authorized to work in the United States without sponsorship. 

Apply Here