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Tier-1 employer, mid-level Data Scientist title, metro location, and generalist ML requirements increase competition.
Requires specialized ML and finance/ERP integration experience so skills are transferable but domain expertise matters.
Explicit 4–6 years requirement plus mandatory ML/Python/production deployment and degree requirements increase filtering.
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Design, develop, deploy, and maintain AI/ML solutions to automate and enhance Record to Report (R2R) finance processes, including anomaly detection and predictive analytics.
Build Python-based data pipelines and maintain machine learning models or AI agents to improve R2R productivity and scalability.
Collaborate with Enterprise AI, IT, and Data Platform teams to integrate solutions ensuring alignment with security and infrastructure standards.
Bachelor’s degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.
4–6+ years of hands-on experience in data science, analytics, or machine learning (minimum 3 years work experience accepted with advanced degree substitutions).
Strong proficiency in Python and data science libraries with experience in building, training, and deploying ML models or AI solutions.
Work Experience Required: 3+ years in Data Science or related field (advanced degrees can substitute up to 2 years).
Experienced in hands-on AI and machine learning solution delivery with focus on finance automation use cases such as R2R anomaly detection and predictive analytics.
Capable of developing and maintaining scalable, explainable, and production-ready AI/ML pipelines and monitoring model performance post-deployment.
Able to partner effectively with IT and data platform teams for integration and alignment with enterprise security and architectural standards.