Data Annotator - Data Scientist

• Data Analysis and Interpretation: Analyze complex datasets, identify meaningful patterns, evaluate trends, and assess the accuracy of data-driven conclusions.

• Statistical Analysis and Evaluation: Review statistical methodologies, probability concepts, hypothesis testing, regression models, and statistical interpretations.

• Machine Learning Evaluation: Assess machine learning algorithms, model selection strategies, feature engineering techniques, and predictive modeling workflows.

• Technical Data Annotation: Produce high-quality annotations for data science-related datasets, including analytical problems, Python code, statistical outputs, and technical documentation.

• AI-Generated Output Evaluation: Evaluate AI-generated analytical responses for mathematical accuracy, logical consistency, statistical correctness, and contextual relevance.

• Python Code Assessment: Review Python-based data science implementations, identify logical errors, evaluate coding approaches, and recommend technically sound improvements.

• SQL and Data Query Evaluation: Analyze SQL queries, data extraction processes, joins, aggregations, and query accuracy.

• Model Performance Assessment: Evaluate machine learning models using appropriate performance metrics, validation techniques, and statistical measures.

• Prompt and Response Evaluation: Review prompts and AI-generated responses to assess technical accuracy, completeness, relevance, and adherence to instructions.

• Technical Documentation: Provide clear explanations, structured annotations, technical justifications, and actionable feedback for evaluation tasks.

• Quality Assurance: Maintain high standards of annotation accuracy and consistency while following project-specific guidelines and evaluation requirements.

Requirements:

• 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.

• Strong hands-on programming experience with Python.

• Practical experience using data science libraries and frameworks such as Pandas, NumPy, and Scikit-learn.

• Solid understanding of statistics, probability, hypothesis testing, and mathematical concepts.

• Experience with machine learning algorithms, predictive modeling, feature engineering, and model evaluation techniques.

• Proficiency in SQL and experience querying, manipulating, and analyzing structured datasets.

• Familiarity with data visualization tools such as Matplotlib, Seaborn, Tableau, or Power BI.

• Ability to independently analyze complex datasets and assess existing analytical models and implementations.

• Familiarity with Jupyter Notebooks and collaborative data science development workflows.

• Strong analytical thinking, problem-solving abilities, and technical reasoning skills.

• Excellent written communication skills, with the ability to clearly explain technical decisions, analytical findings, and evaluation outcomes.

• A detail-oriented and results-driven approach, with the ability to work independently and consistently meet quality standards.

Benefits:

• Competitive hourly compensation: USD 30–80, depending on project-specific compensation terms.

• Fully remote working arrangement, allowing you to work from anywhere in India.

• Flexible part-time engagement with a minimum commitment of 2 dedicated hours per day.

• Flexible working hours, subject to project deadlines and delivery requirements.

• Opportunity to contribute to advanced AI training and evaluation projects.

• Hands-on exposure to data science, statistical modeling, machine learning, and AI development methodologies.

• Opportunity to and strengthen your technical expertise through practical analytical challenges.

• Flexible project-based work that can complement other professional commitments.