Data Annotator - Data Scientist

New

JobgetherAI and data science

Fully remote, allowing you to work from anywhere in IndiaPart-TimeSenior

Salary30 - 80 USD per hour

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Job Details

Experience: 2–6 years of professional experience, with at least 2 years in Data Science, Applied Science, Data Analytics, Machine Learning, or a related technical field.
Required Skills: PythonSQLMachine LearningNumpyPandasscikit-learn

Have 2–6 years of professional experience, including at least 2 years in Data Science, Applied Science, Data Analytics, Machine Learning, or a related technical field.
Have strong hands-on programming experience with Python.
Have practical experience with data science libraries and frameworks such as Pandas, NumPy, and Scikit-learn.
Understand statistics, probability, hypothesis testing, and mathematical concepts.
Have experience with machine learning algorithms, predictive modeling, feature engineering, and model evaluation techniques.
Be proficient in SQL and experienced in querying, manipulating, and analyzing structured datasets.
Be familiar with data visualization tools such as Matplotlib, Seaborn, Tableau, or Power BI.
Be able to independently analyze complex datasets and assess analytical models and implementations.
Be familiar with Jupyter Notebooks and collaborative data science development workflows.
Have strong analytical thinking, problem-solving, and technical reasoning skills.
Have excellent written communication skills for explaining technical decisions, analytical findings, and evaluation outcomes.
Be detail-oriented and able to work independently while consistently meeting quality standards.

Analyze complex datasets, identify patterns and trends, and assess data-driven conclusions.
Review statistical methodologies, probability concepts, hypothesis testing, regression models, and statistical interpretations.
Assess machine learning algorithms, model selection strategies, feature engineering, and predictive modeling workflows.
Annotate data science datasets, including analytical problems, Python code, statistical outputs, and technical documentation.
Evaluate AI-generated analytical responses for mathematical accuracy, logical consistency, statistical correctness, and contextual relevance.
Review Python-based data science implementations, identify logical errors, and recommend improvements.
Analyze SQL queries, data extraction processes, joins, aggregations, and query accuracy.
Evaluate machine learning models using performance metrics, validation techniques, and statistical measures.
Review prompts and AI-generated responses for technical accuracy, completeness, relevance, and adherence to instructions.
Provide technical explanations, justifications, and actionable feedback while maintaining annotation accuracy and consistency.

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