edX Boot Camps Jobs

About This Role:

As a Central Grader, your primary goal is to deliver a faster turnaround for homework grades and consistently high-quality feedback. We succeed as a team in providing students grades and feedback that is detailed, clear, and supports the journey of learning by helping students understand where they can improve and/or advance. Central graders play an integral part of the student experience ensuring that students and local instructional staff know how a student is performing and where best to add support.

Skills & Requirements:

• Minimum 1-3 years of work experience, or have graduated from one of our programs
• Possess working knowledge of content/stack
• A positive attitude
• Remote working and communication skills

What You Will Do:

• Prepare and stay up to date with specific curriculum expectations and core concepts
• Grade student homework submissions in BCS/Canvas within 24-48 hours of submission
• Articulate clear and thorough feedback that supports the specific grade assigned
• Identify student errors and communicate explicitly for visibility to local instructional team
• Check submissions for plagiarism prior to grading and notify the team as needed
• Encourage students' progression of learning according to our pedagogy
• Communicate to the student in a way that also keeps the instructional staff in mind

Logistics:

• Hourly employment
• Time-management is a must
• Remote position
• Stable internet connection required
• 15+ hr/week shifts based on need
• Schedule flexibility is very helpful.
• Must have a laptop/desktop with reliable internet access

Our Centralized Graderssupport students enrolled in University Bootcamps across the globe in the following areas:

• Financial Technologies
  • • Financial Fundamentals in Time-Series Analysis, Financial Ratios, and Financial Analysis
    • Python 3, Pandas, MAtplotlib, API Interactions, and JupyterLab
    • Financial libraries and Tools like NumPy, SciPy, and Quantopian
    • Execution Algorithms, Monte Carlo Simulations, Risk-Data Aggregation, Forecasting, Financial Modeling, Modern Portfolio Theory, and Logistic Regression
    • Machine Learning using Algorithmic Trading, Random Forests, k-Nearest Neighbor (kNN), Support ZVector Machines (SVM), Linear Regression, and Scikit-learn
    • Blockchain and Cryptocurrency experience in Solidity, Ethereum, Consensus Algorithms, Transactions, Validation, Distributed Ledger, Bitcoin and Bitcoin Cash, and Mining