Job Title: AI Scientist
Department: Artificial Intelligence
Job Level/Grade: VP
POSITION SUMMARY:
Metropolitan Commercial Bank (the “Bank”) is seeking a VP-level Senior AI Scientist to design, build, validate, and operationalize production-grade AI/ML, Generative AI, and agentic 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 assistants/copilots, knowledge assistants, workflow automation, 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/data platform and requires working familiarity with Microsoft AI Foundry / Foundry Agent Service, Azure AI Search, and modern agentic workflow patterns.
ESSENTIAL FUNCTIONS AND RESPONSIBILITIES (Duties which are critical/central to accomplishing the purpose of the job)
Duties & Responsibilities:
• Applied AI/ML, GenAI, and agentic workflow development:
· Design and implement models and intelligent workflows for fraud detection, AML alert scoring/triage, AI-generated credit memo drafting, underwriting decision support, contact center AI assistants/copilots, and personalization for commercial/treasury use cases.
· Leverage modern methods: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic retrieval, embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML; design agentic workflows using tool/skill design, prompt and context management, subagent orchestration, and secure integration via APIs, Model Context Protocol (MCP), and Agent-to-Agent (A2A) patterns where appropriate.
· Embed explainability (e.g., SHAP, interpretable scorecards/monotonic models) and conduct pre-/post-deployment bias testing with documented remediation.
• Model validation, documentation & governance (SR 11‑7):
· 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, prompt/version governance, and periodic review cadence.
· Define monitoring, drift thresholds, retraining triggers, groundedness/hallucination evaluation criteria, and safe rollback/kill-switch procedures; maintain human-in-the-loop checkpoints for high-impact decisions.
• Productionization, AgentOps & MLOps on Snowflake / Azure:
· Package, deploy, and operate models and agentic workflows via CI/CD, containerization, model/prompt registries, and observability; instrument KPIs/KRIs, traces, 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, Microsoft AI Foundry / Foundry Agent Service, Azure AI Search, Azure ML, Databricks).
• Regulatory, privacy, and cybersecurity alignment:
· 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/agentic stacks, including controls for prompt injection, data leakage, and model abuse.
• Third‑party AI & data stewardship:
· Support due diligence, testing, and ongoing monitoring of vendor AI/data providers, hosted model APIs, and AI-enabled platforms per SR 23-4; evaluate conceptual soundness, fairness, security, and data-use restrictions.
· 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.
• Cross‑functional partnership:
· Collaborate with AI Working Group, 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, limitations, and control evidence clearly to technical and non-technical stakeholders (management committees, AI governance forums, and examiners).
• Innovation, coaching, and best practices:
· Evaluate emerging ML/GenAI methods, agent frameworks, MCP/A2A interoperability patterns, Microsoft AI Foundry capabilities, and governance tooling; lead POCs within established control gates.
· Mentor junior staff; promote responsible AI practices, documentation standards, and reproducibility.
KNOWLEDGE, SKILLS and ABILITIES (Required for this job.):
· Expertise in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow), NLP/LLMs, LangChain and/or comparable orchestration frameworks, embeddings/vector search, and classical ML.
· Strong experience with agentic workflow design, including tool/skill design, prompt/context management, subagent patterns, human-in-the-loop control design, and secure integration of AI systems with enterprise tools, APIs, Model Context Protocol (MCP), and Agent-to-Agent (A2A) patterns.
· RAG proficiency, including document chunking/indexing, vector search, citation grounding, agentic retrieval, and evaluation of answer quality, groundedness, and hallucination risk.
· MLOps / LLMOps / AgentOps proficiency with CI/CD, containerization (Docker), registries, prompt/version management, tracing/observability, and evaluation harnesses; cloud ML (Snowflake-native ML, Microsoft AI Foundry / Foundry Agent Service, 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; strong grasp of SR 11-7 lifecycle, model documentation, and operational monitoring within three lines of defense governance; ability to produce interpretable reason codes for ECOA/Reg B adverse actions as applicable.
· Excellent communication, curiosity, and problem-solving mindset; ability to translate technical detail to business/risk stakeholders, drive decisions, and balance innovation with disciplined risk management.
EDUCATION/EXPERIENCE REQUIREMENTS (minimum education and/or years of experience required to perform the job.) – check appropriate education category:
☐ High school diploma or equivalent work experience.
☐ College degree or equivalent work experience.
☒ Advanced degree or equivalent experience).
Required Years of Work Experience:
PREFERRED KNOWLEDGE SKILLS AND EXPERIENCE FOR THIS JOB:
· Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
· Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, model registry, pipeline orchestration, and Kubernetes; experience deploying AI/ML services in production a plus.
· Hands-on with Microsoft AI Foundry / Foundry Agent Service, Azure AI Search, and/or Microsoft Agent Framework / Semantic Kernel; experience building production RAG and agentic workflows a plus.
· Experience with agentic patterns such as tool/skill catalogs, subagent orchestration, MCP server/client integrations, A2A interoperability, and evaluation of groundedness, hallucination, safety, latency, cost, and tool-call accuracy.
· Fairness toolkits and XAI frameworks; experience preparing AI/ML or GenAI systems for validation, audit, or regulatory exam discussions; familiarity with SR 23-4 (third-party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber), and current AI governance expectations in regulated environments.
· Ability to work in a constantly evolving environment
· 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.
SUPERVISORY RESPONSIBILITIES (Yes/No):
☒ Yes
☐ No
Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.