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Strong analytics brand, mid-level ML role, popular Data Scientist title, and hybrid work increase competition.
Applied ML/LLM skills transfer across industries, though enterprise data governance increases domain specificity.
Explicit 5+ years requirement plus mandatory ML/LLM, Python, SQL, governance, and leadership skills.
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Define and implement evaluation frameworks for AI agents focusing on precision, recall, robustness, and correctness.
Build, curate, and analyze training and evaluation datasets including error analysis on AI outputs like SQL generation and data products.
Collaborate with AI engineers to enhance AI agent performance, establish quality metrics, and validate AI-generated artifacts such as data models and queries.
3–5+ years of hands-on experience in data science or AI-related roles.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
Strong proficiency in Python, SQL, and experience with data-intensive systems and AI/LLM environments.
Experience designing and evaluating AI/LLM-based systems including governance, safety, and quality controls in AI contexts.
Experienced in leading data science teams and delivering enterprise-grade AI capabilities with measurable impact.
Strong expertise in ML/DL/RL, statistical validation, and building evaluation datasets and benchmarks for AI/LLM systems.
Comfortable working in cloud-native environments with proficiency in modern data transformation and governance practices.