AI Scientist

Metropolitan Commercial Bank • New York, NY

1d • $130,000 - $200,000 • Hybrid

About The Position

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.
Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.
The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.
We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.
Position Summary:
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. 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).

Requirements

• 6+ years of relevant work experience.
• 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 ML proficiency: Snowpark Python, UDFs/UDTFs, Tasks/Streams; ability to build and operate ML workflows inside Snowflake.
• 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.
• Strong grasp of SR 11‑7 lifecycle, model documentation, and operational monitoring within three lines of defense governance.
• Excellent communication; ability to translate technical detail to business/risk stakeholders and drive decisions.
• Curiosity and problem‑solving mindset; ability to balance innovation with disciplined risk management.
• Must have excellent written and verbal communication skills
• Must be a good listener and good teacher
• Demonstrate analytical, troubleshooting and problem-solving skills
• The ability to learn new technologies quickly
• Self-directed individual with technology and communication skills.
• Ability to take in multiple sources of information with an understanding of the bigger picture need, want, and operation of the Bank.
• Collaborative team-player who can find creative and practical solutions in a dynamic work environment.
• Ability to handle ambiguity, juggle multiple matters at once, and quickly and seamlessly shift from one situation or task to another.

Nice To Haves

• Master’s or PhD in a relevant field (Computer Science, Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML, NLP, or model interpretability.
• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions; experience with feature management/registry tooling a plus. model registry and pipeline orchestration; Kubernetes a plus.
• RAG architectures, vector databases, prompt engineering, and LLM evaluation (accuracy, hallucination, safety).
• Fairness toolkits and XAI frameworks; experience preparing models for validation, audit, or regulatory exam discussions.
• Familiarity with SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).

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.
• 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 stacks.
• Support due diligence, testing, and ongoing monitoring of vendor AI/data providers per SR 23‑4; evaluate conceptual soundness, fairness, and security.
• Negotiate/verify contractual controls (no vendor training on MCB/NPI, subprocessors disclosure, audit rights, exit/portability).
• Ensure AEDT compliance (NYC Local Law 144) for any HR-related AI tools.
• Collaborate with Model Risk, Compliance/Legal, Cyber/IT, Data Privacy, Internal Audit, and business owners to meet objectives while staying within risk appetite.
• Communicate complex results, risks, and limitations clearly to technical and non‑technical stakeholders (management committees, examiners).
• Evaluate emerging ML/GenAI methods, LLM evaluation techniques, Snowflake‑native capabilities (e.g., vector search, orchestration), and governance tooling; lead POCs within established control gates.
• Mentor junior staff; promote responsible AI practices, documentation standards, and reproducibility.

Benefits

• Potential Salary: $130,000 - $200,000 annually
• This salary range reflects base wages and does not include benefits, bonus, or incentive pay.
• 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.
• Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior