Data Annotator - Data Scientist

Accountabilities: 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 apply and strengthen your technical expertise through practical analytical challenges. Flexible project-based work that can complement other professional commitments.