Solidity Jobs in Honolulu, Hawaii, United States

About this role:

In a virtual environment intended to support curriculum-related questions, "Learning Assistants" play an integral role in building trust and executing engagement with students to "assist" their "learning" experience. Learning Assistants have an opportunity to play a more virtual-friendly role that directly relates to the Bootcamp journey and adds meaning to our pedagogy of learning in a virtual capacity.

Skills & Requirements:

• Excellent written and verbal communication skills.
• Excellent organizational skills.
• Ability to self-manage daily remote work.
• A thorough, detail-oriented work >
• Proficiency with software engineering tools.
• Ability to attend and contribute to virtual meetings.
• Ability to analyze complex technical information.
• Ability to diagnose and troubleshoot complex, technical problems quickly and efficiently.
• A logical, analytical, and creative approach to problem-solving
• Have a patient and positive attitude.
• Able to infuse empathy, support, encouragement, and fun into the learning environment.
• Minimum of 6 months of work experience or graduation from a Bootcamp >
• As listed below, experience with specific technologies for your program of application.

What You Will Do:

• Prepare and stay updated with specific curriculum and core concepts of your assigned support program.
• Contribute to team-wide support goals by meeting and exceeding personal key performance indicator goals.
• Provide world-class student support while adhering to our support model to solve and respond to curriculum-based technical questions.
• Craft easy-to-understand responses that guide students to discover the answer to their questions.
• Attend and contribute to daily meetings at the start and end of every shift.
• Contribute to a culture of trust by adhering to all operational and system procedures.
• Collaborate with other team members to troubleshoot, support, or redirect students' questions.
• Attend training and development sessions as determined by management..
• Infuse empathy, support, encouragement, and fun into the students' learning progression.
• Be available to work days, nights, weekends, and holidays as necessary.

Logistics:

• This is a remote, part-time position (W2-based employment). You may work from anywhere!
• A laptop, camera, microphone/headset, and reliable high-speed internet access are required.
• Personalized scheduling! While shift start and end times are predetermined, you choose the days and shifts you'd like to work!
• Hourly employees with 2u/EdX are limited to a maximum of 29 hours worked per week.

Learning Assistant Scheduling:

Learning Assistants will work on a predefined, personalized shift schedule each week. Based on availability, Learning Assistants may choose any combination of days and shift hours.

Learning Assistants must commit to working a minimum of 8 hours each week. (To best meet the needs of our learners, shift start and end times cannot be modified.)

Shift Start and End Times:

• 12a - 5a ET - 5 Hours
• 5a - 10a ET - 5 Hours
• 10a - 6p ET - 8 Hours
• 6p - 10p ET - 4 Hours
• 8p - 12a ET - 4 Hours

Other Perks:

• Maintain your current career while teaching
• Hone your skills by teaching and mentoring others
• Expand your network by joining the thousands of industry professionals who comprise our Instructional Staff
• Experience the gratitude and fulfillment that comes along with teaching and giving back to the tech community
• Management skills development
• Conflict resolution development
• Coaching and mentoring

Our FinTech Learning Assistants support students enrolled in University Bootcamps across the globe in the following areas:

• 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