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Tier-1 employer, mid-level experience, and metro location increase applicant density.
High because Palantir Foundry, regulatory model-risk, and actuarial econometrics specialization limit cross-industry transferability.
Explicit 4–7 years plus mandatory Foundry, MLOps, and regulatory model-governance requirements raise strictness.
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Develop and operationalize GenAI and AI solutions using Palantir Foundry, including prompt engineering, retrieval-augmented generation, and multi-agent systems.
Build and deploy econometric, statistical, and machine learning models (e.g., GLMs, XGBoost) within scalable Foundry data pipelines using Python, PySpark, and SQL.
Ensure data quality, model risk management compliance (SR 11-7, NIST, ISO 42001), and collaborate cross-functionally to deliver production-grade AI and analytics solutions integrated into client workflows.
Degree in statistics, mathematics, electrical engineering, physics, econometrics, computer science, or a related technical field.
4-7 years of relevant work experience in AI, data science, or risk modeling domains.
Experience with Palantir Foundry platform, including data pipelines, ontologies, and deployment.
Familiarity with model risk management and AI governance standards (e.g., SR 11-7, Colorado SB21-169, NIST frameworks).
Strong expertise in large-scale data engineering, machine learning model development, and deployment using Palantir Foundry and Spark/PySpark environments.
Comfortable managing regulatory compliance and auditability related to data pipelines and models in complex business environments.
Experienced working collaboratively with cross-functional teams (data engineers, architects, scientists) to deliver client-focused AI and analytics solutions that integrate into operational processes.