AI Scientist

Come work with us:

Metropolitan Commercial Bank (the "Bank") is a full‑service commercial bank based in New York City. The Bank provides a broad range of business, commercial, and personal banking products and services to individuals, small businesses, private and public middle‑market and corporate enterprises and institutions, municipalities, and local government entities.

Metropolitan Commercial Bank was named one of Newsweek's Best Regional Banks and Credit Unions 2024. The Bank was ranked by Independent Community Bankers of America among the top ten successful loan producers for 2023 by loan category and asset size for commercial banks with more than $1 billion in assets. Kroll affirmed a BBB+ (investment grade) deposit rating on January 25, 2024.

For the fourth time, MCB has earned a place in the Piper Sandler Bank Sm‑All Stars Class of 2024.

Metropolitan Commercial Bank operates banking centers and private client offices in Manhattan, Boro Park, Brooklyn and Great Neck on Long Island in New York State.

The Bank is a New York State chartered commercial bank, a member of the Federal Reserve System and the Federal Deposit Insurance Corporation, and an equal housing lender. The parent company of Metropolitan Commercial Bank is Metropolitan Bank Holding Corp. (NYSE: MCB).

Metropolitan Commercial Bank (the "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. This role focuses on high‑impact use cases‑fraud detection, AML alert optimization, AI‑assisted credit memo generation for underwriting decision support, contact center AI assistant/copilots, and personalization for treasury/commercial clients‑delivered with rigorous governance, explainability, fairness testing, privacy‑by‑design, cybersecurity, and model lifecycle controls aligned to SR 11‑7 and MCB's Trustworthy & Responsible AI Principles.

The role emphasizes Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake‑native ML).

We have a flexible work schedule where employees can work from home one day a week.

• 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.
• Leverage modern methods:
  Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG), embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML.
• Embed explainability (e.g., SHAP, interpretable scorecards/monotonic models) and conduct pre‑/post‑deployment bias testing with documented remediation.

• Produce audit‑ready documentation (methodology, assumptions, data lineage, limitations, testing) and register models in the inventory with owners/materiality.
• Facilitate independent validation/effective challenge; obtain required approvals before deployment; maintain change management and periodic review cadence.
• Define monitoring, drift thresholds, retraining triggers, and safe rollback/kill‑switch procedures; maintain human‑in‑the‑loop checkpoints for high‑impact decisions.

• Package, deploy, and operate models via CI/CD, containerization, and model registry; instrument KPIs/KRIs and alerting dashboards. Operate models natively on Snowflake using Snowpark Python, UDFs/UDTFs, Tasks/Streams, and secure external access where required.
• Partner with Engineering to integrate models via secure APIs/batch; ensure scalability, resiliency, and observability in cloud/on‑prem (e.g., Snowflake, Azure ML, Databricks).

• 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…