Junior Data Scientist Engineer - Remote Job, 1+ Year Experience

This is a remote position.

Junior Data Scientist Engineer  - Remote Job, 1+ Year Experience

Annual Income: $64K - $74K

A valid work permit is necessary in the US

About us: Patterned Learning is a platform that aims to help developers code faster and more efficiently. It offers features such as collaborative coding, real-time multiplayer editing, and the ability to build, test, and deploy directly from the browser. The platform also provides tightly integrated code generation, editing, and output capabilities.

Skills and Abilities:

• Strong knowledge of R or Python for data analysis and modeling.
  

• Proficiency in statistical programs such as R, SAS, MATLAB, or Python.
  

• Familiarity with spreadsheets (VBA) and database applications (Access, Oracle, SQL, or equivalent technology).
  

• Basic understanding of SQL, Javascript, XML, JSON, and HTML.
  

• Ability to learn new methods quickly and work under deadlines.
  

• Excellent teamwork and communication skills.
  

• Strong analytical and problem-solving abilities.
  

• Basic understanding of SQL, Javascript, XML, JSON, and HTML.
  

Preferred:

• Knowledge of actuarial concepts and life, health, and/or annuity products.
  

• Experience with statistical modeling techniques such as GLM, Decision Trees, Time Series, Regression, etc.
  

• Familiarity with Microsoft DeployR.
  

• Exposure to insurance risk analysis.
  

• Basic experience in computational finance, econometrics, statistics, and math.
  

• Knowledge of SQL and VBA.
  

• Familiarity with R or Python for predictive modeling
  

Why Patterned Learning LLC?

Patterned Learning can provide intelligent suggestions, automate repetitive tasks, and assist developers in writing code more effectively. This can help reduce coding errors, improve productivity, and accelerate the development process.

The pattern recognition is particularly relevant in the context of coding. Neural networks, especially deep learning models, are commonly employed for pattern detection and classification tasks. These models simulate human decision-making and can identify patterns in data, making them well-suited for tasks like code analysis and generation.