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Centralized Grader FinTECH Program Remote

edX Boot Camps

Live

$54k - $77k · Senior Full-time Posted about 3 years ago 2 min read

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

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