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$9

$25

$76

How much do part time data encoder jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for part time data encoder in New York is $25.01, according to ZipRecruiter salary data. Most workers in this role earn between $13.67 and $21.55 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Part Time Data Encoder position, and why are they important?

To excel as a Part Time Data Encoder, you should possess strong typing proficiency, attention to detail, and a high school diploma or equivalent. Familiarity with data entry software, spreadsheets (such as Microsoft Excel or Google Sheets), and sometimes database management systems is often required. Excellent time management, organizational skills, and the ability to maintain confidentiality help candidates stand out. These abilities ensure data is entered accurately and efficiently, supporting reliable record-keeping and business operations.

What are some typical challenges faced by part-time data encoders, and how can they be managed?

Part-time data encoders often face challenges such as tight deadlines, repetitive tasks, and the need to maintain high accuracy over extended periods. It’s important to develop effective time management strategies and take regular short breaks to stay focused and avoid errors. Most companies provide training and encourage open communication with supervisors if questions or issues arise. Working collaboratively with other team members can also help in resolving discrepancies and maintaining data quality. By staying organized and proactive, part-time data encoders can successfully manage these challenges and perform well in their roles.

What is a Part Time Data Encoder job?

A Part Time Data Encoder is responsible for inputting, updating, and maintaining accurate data in computer systems or databases. They typically work with spreadsheets, documents, or software to ensure information is correctly recorded and organized. This role requires attention to detail, typing proficiency, and basic computer skills. Part-time data encoders may work in various industries, such as healthcare, finance, or retail, depending on the organization's needs.

What are the most commonly searched types of Data Encoder jobs in New York? The most popular types of Data Encoder jobs in New York are:
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Part Time Data Encoder jobs near you
Infographic showing various Part Time Data Encoder job openings in New York as of July 2026, with employment types broken down into 100% Part Time. Highlights an 100% In-person job distribution, with an average salary of $52,021 per year, or $25 per hour.
Adjunct Lecturer, Applied Deep Learning and AI (On-Campus, Fall '26)

Adjunct Lecturer, Applied Deep Learning and AI (On-Campus, Fall '26)

Columbia University

New York, NY • On-site

$11K - $13K/mo

Part-time

Re-posted 19 days ago

Job description

Company Description

Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society.

The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through seventeen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good.

Job Description

The School of Professional Studies seeks experienced industry professionals to serve as part-time Lecturer for a graduate-level course in Applied Deep Learning and AI. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models. Lecturers are the primary instructors for courses and an invaluable component of the faculty community.

Responsibilities

  • Lead class lectures, instructional activities, and classroom discussions. Attend all class sessions.

  • Monitor and address student concerns and inquiries.

  • Evaluate and grade student work and assessments.

  • Conduct office hours.

Qualifications

Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training and a record of accomplishment as practitioners in an applied industry setting. 

Requirements

  • Doctorate degree in Computer Science, Data Science, or a related field.
  • Proficient in Python and familiar with deep learning frameworks (e.g., TensorFlow, PyTorch).
  • Deep Learning Knowledge: Strong understanding of CNNs, RNNs, LLMs, Reinforcement Learning, and model evaluation techniques.
  • Hands-on experience with deep learning projects and data manipulation.

Preferred Skills & Experience

  • 10+ years of related professional experience. 

  • 2+ years of University teaching experience, ideally at the graduate level.

  • Strong verbal and written skills for explaining concepts clearly.

Additional Information

Salary range: $11,000 - $13,000 per semester long course
Please submit a resume inclusive of university teaching experience.

All your information will be kept confidential according to EEO guidelines.
Columbia University is an Equal Opportunity Employer / Disability / Veteran

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