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Remote role, popular Data Scientist title, and mid-level technical requirements increase candidate competition.
Specialized Responsible AI and GenAI expertise limits cross-industry transferability.
Requires ML/GenAI and Responsible AI expertise and Python; no explicit years, so filtering is moderate.
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Partner with AI Factory teams as a Responsible AI expert, embedding evaluation workflows, guardrails, and measurement practices.
Execute Responsible AI evaluations focusing on hallucination, fairness, transparency, robustness, and other model risks using benchmark datasets and structured methods.
Conduct research and prototyping of emerging Responsible AI techniques, supporting transition of successful methods into platform capabilities.
Bachelor’s or Master’s degree in computer science, data science, statistics, or related quantitative discipline (or equivalent experience).
Experience developing, validating, or evaluating machine learning models in production or pre-production environments.
Strong proficiency in Python and working with data for ML analysis, evaluation, and experimentation.
Familiarity with GenAI or LLM systems including model behavior analysis, safety testing, or explainability techniques.
Experience working in matrixed environments collaborating closely with engineering, QA, and product teams integrating Responsible AI into AI/ML workflows.
Background in statistical analysis, experimental design, and model performance assessment applied in enterprise AI or ML production contexts.
Proven ability in research or applied development of Responsible AI methods such as hallucination detection, explainability (e.g. SHAP, LIME), fairness assessment, or model risk evaluation.