Data Scientist

Translate complex business challenges into clear, measurable data science and machine learning problems with defined objectives and success metrics.

• Develop, train, validate, and continuously improve classical machine learning and NLP models using Python and modern data science libraries.
• Build complete modeling pipelines, including data preparation, feature engineering, model training, validation, evaluation, and production handover.
• Utilize BigQuery and BigQuery ML for large-scale data processing, experimentation, model development, and analytical workflows.
• Prototype Generative AI solutions using Large Language Models (LLMs), embeddings, prompting, and basic Retrieval-Augmented Generation (RAG) methods.
• Design robust experimental and evaluation frameworks for both classical ML and LLM-based solutions.
• Assess model performance, quality, fairness, explainability, and business impact using appropriate AI/ML metrics.
• Create model cards, technical documentation, and governance materials to support responsible AI practices.
• Collaborate with ML Engineers, Data Engineers, Product teams, and business stakeholders to operationalize solutions effectively.
• Mentor junior team members, review analytical work, and contribute to reusable standards and knowledge sharing in the Data Science community.
• Apply expertise in Python, machine learning, NLP, Generative AI, RAG, BigQuery ML, experimentation, and model governance.

Demonstrate over 10 years of experience in data science, machine learning engineering, or related applied AI roles, including translating ambiguous business needs into defined analytical or AI problems.