Junior Data Scientist in भारत

Junior Data Scientist

Employment Type: Contractor
Work Arrangement: Remote
Eligible Locations: English-speaking countries

Compensation

Hourly Rate: $150–$200 per hour

Annualized Full-Time Equivalent: $288,000–$384,000 per year

The annualized figure is based on 40 hours per week and 48 working weeks per year. It is for comparison only; actual earnings depend on project availability, tasks completed, and hours worked.

About the Role

We are seeking Junior Data Scientists with strong quantitative skills to evaluate and improve AI model performance across data science, statistics, machine learning, experimentation, and quantitative reasoning.

In this remote contract role, you will assess AI-generated solutions, develop technical prompts and reference solutions, and identify statistical errors, flawed methodologies, and weaknesses in quantitative reasoning.

This opportunity is suited to data science and quantitative professionals with relevant industry experience, strong analytical judgment, and the ability to explain technical concepts clearly. Despite the junior title, the role requires rigorous technical evaluation and the ability to develop expert-level assessment materials.

Key Responsibilities

AI Model Evaluation

• Evaluate AI-generated outputs involving data science, statistics, machine learning, and quantitative problems.

• Assess the accuracy, validity, and methodological soundness of model responses.

• Identify statistical errors, flawed assumptions, and weaknesses in quantitative reasoning.

• Provide structured feedback to help improve AI model capabilities.

Technical Prompt & Solution Development

• Create challenging technical prompts covering data analysis, statistical reasoning, machine learning, and experimentation.

• Develop relevant datasets and reference solutions for evaluating model performance.

• Apply appropriate quantitative methods to verify the correctness of solutions.

• Ensure evaluation materials accurately reflect the intended problem and expected results.

Statistical Analysis & Quality Assessment

• Review analytical approaches for methodological errors and statistical weaknesses.

• Evaluate the application of machine learning methods and experimental techniques.

• Identify incorrect interpretations, unreliable conclusions, and gaps in quantitative reasoning.

• Document findings and explain technical issues clearly.

Required Qualifications

• At least one year of professional experience at a leading company in technology, finance, research, or a related field.

• At least part of that professional experience must fall within the past three years.

• Strong knowledge of data science, statistics, machine learning, or quantitative analysis.

• Proficiency in Python for data analysis and quantitative problem-solving.

• Familiarity with tools such as pandas and relevant data modeling techniques.

• Ability to evaluate analytical methodologies and identify statistical errors.

• Strong written communication skills, particularly when explaining technical and statistical concepts.

• Based in an English-speaking country.

Key Skills

• Python

• Data Science

• Machine Learning

• Statistics

• Data Analysis

• Experimentation

• pandas

• Data Modeling

• Quantitative Reasoning

• Statistical Methodology

• AI Model Evaluation

• Technical Prompt Development

• Reference Solution Development

• Analytical Problem-Solving

• Written Technical Communication

Work Environment

This is a remote, hourly contract opportunity focused on evaluating AI models and developing technical assessment materials.

You will work on quantitative problems, review model-generated solutions, create reference materials, and provide structured feedback to improve the accuracy and reliability of AI systems.

What Success Looks Like

Success means accurately evaluating AI-generated solutions, identifying statistical and methodological weaknesses, and producing high-quality prompts, datasets, and reference solutions.

Your work should demonstrate sound quantitative reasoning, technical accuracy, and clear communication while helping improve AI performance across data science and machine learning tasks.