Data Annotator - Data Scientist

About the Role
We are looking for experienced Data Scientists, Applied Scientists, and Analytics Professionals to contribute to specialised data annotation, technical evaluation, and AI model training projects.

This is not a conventional data annotation role. The work involves advanced technical evaluation of statistical concepts, data analysis, machine learning methodologies, Python-based implementations, and AI-generated analytical outputs.

Selected professionals will work on tasks that require strong analytical thinking, practical experience with data science workflows, statistical modelling, machine learning algorithms, and the ability to evaluate complex data-driven problems.

The objective is to improve the accuracy, reasoning capabilities, and reliability of AI models by generating, reviewing, annotating, and evaluating technically complex datasets.

Key Responsibilities

• Data Analysis & Interpretation: Analyse complex datasets, identify patterns, evaluate trends, and assess the accuracy of data-driven conclusions.
• Statistical Analysis & Evaluation: Review statistical methodologies, probability concepts, hypothesis testing, regression models, and statistical interpretations.
• Machine Learning Evaluation: Evaluate ML algorithms, model selection approaches, feature engineering techniques, and predictive modelling workflows.
• Technical Data Annotation: Perform high-quality annotations on data science-related datasets, including analytical problems, Python code, statistical outputs, and technical documentation.
• AI-Generated Output Evaluation: Assess AI-generated analytical outputs for mathematical accuracy, logical consistency, statistical correctness, and relevance.
• Python Code Assessment: Review Python-based data science implementations, identify logical errors, evaluate coding approaches, and suggest technically sound corrections.
• SQL & Data Query Evaluation: Analyse SQL queries, data extraction workflows, joins, aggregations, and query correctness.
• Model Performance Evaluation: Assess model performance using appropriate evaluation metrics, validation techniques, and statistical measures.
• Prompt and Response Evaluation: Analyse prompts and AI-generated responses for technical relevance, accuracy, completeness, and adherence to instructions.
• Documentation & Reasoning: Provide clear technical explanations, annotations, justifications, and structured feedback for evaluation tasks.
• Quality Assurance: Maintain high annotation accuracy and consistency while following project-specific guidelines and evaluation standards.

Required Skills & Qualifications

• 2+ years of professional experience in Data Science, Applied Science, Analytics, Machine Learning, or related technical roles.
• Strong hands-on experience with Python.
• Practical experience with data science libraries such as Pandas, NumPy, and Scikit-learn.
• Strong understanding of statistics, probability, hypothesis testing, and mathematical concepts.
• Experience with machine learning algorithms, predictive modelling, and model evaluation techniques.
• Proficiency in SQL and experience working with structured datasets.
• Familiarity with data visualisation tools such as Matplotlib, Seaborn, Tableau, or Power BI.
• Ability to independently analyse complex datasets and evaluate existing analytical implementations.
• Familiarity with Jupyter Notebooks and collaborative development workflows.
• Strong analytical, problem-solving, and technical reasoning abilities.
• Excellent written communication skills to explain technical decisions and evaluation outcomes.

Time Commitment

• Minimum 2 hours of dedicated work per day.
• Part-time engagement with flexible working hours, subject to project deadlines and task requirements.
• Hourly-based payment for completed work, as per project-specific compensation terms.
• Professionals must be available consistently and meet the expected quality and delivery standards.

Ideal Candidates
This opportunity is suitable for working Data Scientists, Applied Scientists, Data Analysts with strong technical expertise, and professionals with practical experience in statistical analysis, machine learning, predictive modelling, and data-driven problem-solving.