TheStaffed
Our client, a top-tier Management Consulting firm, is seeking a highly skilled
Applied AI Data Scientist
for a top tier US Bank.
Responsibilities and Requirements
Perform statistical analysis, clustering, and probability modeling to drive insights and inform AI-driven solutions
Analyze graph-structured data to detect anomalies, extract probabilistic patterns, and support graph-based intelligence
Build NLP pipelines with a focus on NER, entity resolution, ontology extraction, and scoring
Contribute to AI/ML engineering efforts by developing, testing, and deploying data-driven models and services
Apply ML Ops fundamentals, including experiment tracking, metric monitoring, and reproducibility practices
Collaborate with cross-functional teams to translate analytical findings into production-grade capabilities
Prototype quickly, iterate efficiently, and help evolve data science best practices across the team
Solid experience in statistical modeling, clustering techniques, and probability-based analysis
Hands-on expertise in graph data analysis, including anomaly detection and distribution pattern extraction
Strong NLP skills with practical experience in NER, entity/ontology extraction, and related evaluation methods
An engineering-forward mindset with the ability to build, deploy, and optimize real-world solutions (not purely theoretical)
Working knowledge of ML Ops basics, including experiment tracking and key model metrics
Proficiency in Python and common data science/AI libraries
Strong communication skills and the ability to work collaboratively in fast-paced, applied AI environments
#J-18808-Ljbffr
Applied AI Data Scientist
for a top tier US Bank.
Responsibilities and Requirements
Perform statistical analysis, clustering, and probability modeling to drive insights and inform AI-driven solutions
Analyze graph-structured data to detect anomalies, extract probabilistic patterns, and support graph-based intelligence
Build NLP pipelines with a focus on NER, entity resolution, ontology extraction, and scoring
Contribute to AI/ML engineering efforts by developing, testing, and deploying data-driven models and services
Apply ML Ops fundamentals, including experiment tracking, metric monitoring, and reproducibility practices
Collaborate with cross-functional teams to translate analytical findings into production-grade capabilities
Prototype quickly, iterate efficiently, and help evolve data science best practices across the team
Solid experience in statistical modeling, clustering techniques, and probability-based analysis
Hands-on expertise in graph data analysis, including anomaly detection and distribution pattern extraction
Strong NLP skills with practical experience in NER, entity/ontology extraction, and related evaluation methods
An engineering-forward mindset with the ability to build, deploy, and optimize real-world solutions (not purely theoretical)
Working knowledge of ML Ops basics, including experiment tracking and key model metrics
Proficiency in Python and common data science/AI libraries
Strong communication skills and the ability to work collaboratively in fast-paced, applied AI environments
#J-18808-Ljbffr