Metropolitan Commercial Bank

Metropolitan Commercial Bank

AI Scientist

New York, NY · Vp

Sponsorship not specified$130k-$200kDetected 41 days ago
PythonSQLSnowflakeDatabricksVector DatabasesAzureDockerKubernetesCI/CDMachine LearningDeep LearningTensorFlowPyTorchPandasSparkData EngineeringData ScienceNLPLLMsRAGMLOpsStatisticsCybersecurityAuditing

About the role

  • The role emphasizes Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).
  • Standard 4-day in-office requirement, 1 day remote (of your choosing)
  • Leverage modern methods: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML.

Responsibilities

  • Design and implement models for fraud detection, AML alert scoring/triage, AI-generated credit memo drafting and underwriting decision support, contact center AI assistants, and personalization for commercial/treasury use cases.
  • Design for ECOA/Reg B (adverse action specificity), UDAAP, FCRA, GLBA privacy, and NYDFS 23 NYCRR 500 cybersecurity requirements.
  • Apply privacy-by-design (data minimization, purpose limitation, retention), strong access controls/segregation, and secure SDLC/red teaming for GenAI stacks.
  • Define monitoring, drift thresholds, retraining triggers, and safe rollback/kill-switch procedures; maintain human-in-the-loop checkpoints for high-impact decisions.
  • Support due diligence, testing, and ongoing monitoring of vendor AI/data providers per SR 23‑4; evaluate conceptual soundness, fairness, and security.
  • Collaborate with Model Risk, Compliance/Legal, Cyber/IT, Data Privacy, Internal Audit, and business owners to meet objectives while staying within risk appetite.

Requirements

  • obtain required approvals before deployment
  • Operate models natively on Snowflake using Snowpark Python, UDFs/UDTFs, Tasks/Streams, and secure external access where required.
  • Expertise in Python (pandas, scikit‑learn), deep learning (PyTorch/TensorFlow), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
  • MLOps proficiency with CI/CD, containerization (Docker), registries, and observability; cloud ML (Snowflakes-native ML, Azure ML or Databricks preferred).
  • Snowflake‑native

Nice to have

  • Data engineering competency (SQL, ETL/pipelines, Spark/PySpark)
  • ability to work with structured/unstructured data.
  • Explainability (e.g., SHAP) and fairness testing
  • ability to produce interpretable reason codes for ECOA/Reg B adverse actions as applicable.
  • Excellent communication
  • Curiosity and problem‑solving mindset
  • ability to balance innovation with disciplined risk management.
  • Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions

Skills

  • 6+ years of relevant work experience.
  • Data engineering competency (SQL, ETL/pipelines, Spark/PySpark); ability to work with structured/unstructured data.
  • Strong grasp of SR 11‑7 lifecycle, model documentation, and operational monitoring within three lines of defense governance.
  • Curiosity and problem‑solving mindset; ability to balance innovation with disciplined risk management.
  • Preferred Qualifications & Skills
  • Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
  • RAG architectures, vector databases, prompt engineering, and LLM evaluation (accuracy, hallucination, safety).

Compensation

  • $130,000 - $200,000 annually
  • This salary range reflects base wages and does not include benefits, bonus, or incentive pay.
  • Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.
  • This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
  • MCB maintains a drug free workplace.
  • Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law.

Benefits

  • Metropolitan Commercial Bank is seeking a VP-level Applied AI & Machine Learning Scientist to design, build, and validate production-grade AI/ML and Generative AI solutions in a highly regulated banking environment.
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This listing is sourced directly from Metropolitan Commercial Bank's careers page and normalized into a canonical job model.