Data Analyst
@ Taskify

Hybrid
$60,000
Hybrid
Part Time
Posted 13 hours ago

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XXXXXXXXXX XXXXXXXXX XXXXXXX******* @taskify.com
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Job Details

Data Analyst Overview

Taskify is seeking a skilled Data Analyst to contribute to AI model development. The role involves working with datasets, applying analytical methods, and generating insights to improve AI performance.

Key Responsibilities

  • Identify and source datasets for AI training and research.
  • Clean, structure, and prepare data for analysis and modeling.
  • Apply statistical and machine learning techniques to generate insights.
  • Evaluate AI outputs and provide detailed feedback.
  • Present analytical findings with actionable recommendations.

Qualifications

Bachelor’s degree or higher in Data Science, Computer Science, Machine Learning, Statistics, Mathematics, Economics, Software Engineering, or related fields; 1-2+ years of professional experience in Data Analysis or Data Science; strong knowledge of Python, R, SQL, or other data analysis tools; and experience working end-to-end with datasets.

Preferred Experience

Machine Learning project experience and exposure to applied research, forecasting, or predictive modeling.

Compensation & Benefits

  • Earn up to $50 USD/hour, paid weekly via PayPal or AirTM.
  • Flexible schedule: typically 5-10 hrs/week, up to 40 hrs.
  • Remote-first opportunity with access to cutting-edge AI tools.

Why Join Taskify?

Collaborate with a global community of data experts on innovative projects that directly shape the future of AI while enjoying flexible project selection that matches your expertise.

Key skills/competency

  • Data Analysis
  • Data Science
  • AI
  • Machine Learning
  • Python
  • SQL
  • Data Cleaning
  • Statistical Analysis
  • Reporting
  • Communication

How to Get Hired at Taskify

🎯 Tips for Getting Hired

  • Research Taskify's culture: Study their mission and global projects.
  • Customize your resume: Highlight relevant data projects and tools.
  • Showcase analytical skills: Emphasize Python, SQL, and ML techniques.
  • Prepare for technical questions: Review dataset cleaning and modeling practices.
  • Follow application instructions: Include detailed examples in your cover letter.

📝 Interview Preparation Advice

Technical Preparation

Review Python libraries for data analysis.
Practice SQL query exercises.
Clean and structure sample datasets.
Revisit statistical and ML techniques.

Behavioral Questions

Describe a challenge during data cleaning.
Explain your approach to teamwork remotely.
Share experiences managing tight deadlines.
Discuss handling complex data insights.

Frequently Asked Questions