Data Scientist

Key Responsibilities

• Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
• Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
• Test AI models for potential inaccuracies or biases, validating their reliability across use cases.

Requirements

• Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Mathematics, or a closely related quantitative field.
• 5+ years of professional experience as a Data Scientist or in a closely related analytical role, working on end-to-end machine learning projects.
• Strong proficiency in Python for data analysis and machine learning, including libraries such as pandas, NumPy, scikit-learn, and related tooling.
• Solid background in statistics, experimental design, and applied probability, with experience designing and analyzing controlled experiments.
• Hands-on experience building, evaluating, and deploying machine learning models in real-world business or product environments.
• Advanced SQL skills and comfort working with large, complex datasets from data warehouses or data lakes.
• Minimum C1 English proficiency (written and spoken), with the ability to write clear quantitative explanations and follow detailed English-language guidelines.
• Experience with data visualization tools or dashboards and the ability to present analytical findings to stakeholders.
• Previous experience with AI data training, annotation, or reviewing AI-generated analytical content is a strong plus.
• Highly detail-oriented and systematic, with a strong focus on quality, reproducibility, and careful evaluation of reasoning steps.

Qualifications

Your profile should include a Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Mathematics, or a closely related quantitative field, with 5+ years of professional experience as a Data Scientist or in a closely related analytical role. You should have strong proficiency in Python for data analysis and machine learning, including libraries such as pandas, NumPy, scikit-learn, and related tooling. A solid background in statistics, experimental design, and applied probability, with experience designing and analyzing controlled experiments, is essential. Hands-on experience building, evaluating, and deploying machine learning models in real-world business or product environments is required. Advanced SQL skills and comfort working with large, complex datasets are necessary. Minimum C1 English proficiency (written and spoken) is expected, along with the ability to write clear quantitative explanations and follow detailed English-language guidelines. Experience with data visualization tools or dashboards and the ability to present analytical findings to stakeholders is beneficial. Previous experience with AI data training, annotation, or reviewing AI-generated analytical content is a strong plus. A highly detail-oriented and systematic approach, with a strong focus on quality, reproducibility, and careful evaluation of reasoning steps, is crucial.