Data Scientist

Job Title:

Locations:

Experience:

Employment Type:

Job Summary

• Translate business requirements into data science problems and define measurable success criteria.
• Develop, train, evaluate, and optimize classical machine learning models.
• Define and track AI/ML performance metrics, including accuracy, precision, recall, F1-score, and business-specific KPIs.
• Contribute to LLM development and evaluation, including response quality, relevance, groundedness, and hallucination assessment.
• Apply prompt engineering techniques and understand basic RAG architectures and workflows.
• Design and execute experiments, including hypothesis testing, A/B testing, and model comparisons.
• Perform feature engineering, data preprocessing, model validation, and error analysis.
• Document model assumptions, limitations, evaluation results, risks, and intended use through model cards.
• Collaborate with data engineers, ML engineers, product teams, and business stakeholders to deliver scalable AI solutions.
  

Required Skills

• Strong understanding of problem framing and analytical thinking.
• Hands-on experience with classical ML algorithms and model development.
• Knowledge of AI/ML evaluation metrics and model validation techniques.
• Experience with LLM development and evaluation.
• Basic understanding of prompt engineering and RAG.
• Experience in experiment design, hypothesis testing, and statistical analysis.
• Familiarity with model documentation, model cards, and responsible AI principles.
• Strong Python and SQL programming skills.
• Experience with libraries such as Pandas, NumPy, Scikit-learn, and relevant ML/LLM frameworks.
  

Preferred Qualifications

• Experience evaluating generative AI applications and LLM outputs.
• Familiarity with frameworks such as LangChain or LlamaIndex.
• Exposure to ML experiment tracking and model lifecycle tools.
• Understanding of bias, explainability, reproducibility, and responsible AI practices.