Metropolitan Commercial Bank
Metropolitan Commercial Bank, The Entrepreneurial Bank Since 1999, is headquartered in New York City and operates full-service banking centers in Manhattan; Boro Park, Brooklyn; and Great Neck, Long Island with additional offices in Lakewood, New Jersey, Louisville, Kentucky and Miami, Florida. Metropolitan Commercial Bank is a New York State chartered commercial bank, an FDIC member and an equal opportunity lender. The parent company of Metropolitan Commercial Bank, Metropolitan Bank Holding Corp., is a publicly traded company.
Did you know? Metropolitan Commercial Bank was named by Piper Sandler as one of the Bank & Thrift SMS-All Stars for the year 2022!
Problem solvers and creative thinkers. Bankers, new business builders, creative managers and back-office professionals. Put your talents and entrepreneurial spirit to use where opportunities abound and the chance to make a difference is real. Every individual at Metropolitan Commercial Bank plays a vital role in our collective success.
Whether you’re an experienced banking professional or a recent graduate, working at MCB could be your next step for a challenging and rewarding career.
Every day, we work to improve our capabilities, build leadership skills, and create better outcomes— for every opportunity, at every level.
Exciting work that makes a difference for businesses large and small, and individuals with diverse banking needs. Colleagues and mentors at the top of their fields. Opportunities to take what you know, develop your skills, and grow.
What awaits you at Metropolitan Commercial Bank? The power to make a difference. The opportunity to be at your best.
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Applied AI Analyst
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Come Work With Us:
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 (the “Bank”) is seeking an Applied AI Analyst to support the design, configuration, testing, and monitoring of applied AI, Generative AI, machine learning, and agentic workflow solutions in a highly regulated banking environment. Working under the guidance of AI Scientists and in partnership with engineering, risk, and business teams, this role focuses on internal human-in-the-loop use cases such as AI-assisted loan documentation generation, fraudulent transaction detection models, policy and knowledge copilots, workflow automation, and other productivity solutions. A meaningful portion of the role is dedicated to AI solution intake and delivery support— learning new business domains, engaging business owners, preparing required documentation, and supporting vendor and risk reviews, so that solutions can be built and deployed under strict control gates and documentation standards aligned to internal and external regulations. The role emphasizes strong documentation, human oversight, privacy-by-design, cybersecurity, and disciplined lifecycle controls aligned to internal and external regulations. The role also emphasizes familiarity with Microsoft Foundry / Foundry Agent Service, approved Microsoft Copilot ecosystems, RAG architectures, and enterprise data platforms such as Snowflake.
Standard 4-day in-office requirement, 1 day remote (of your choosing)
Essential Functions & Responsibilities
Applied AI solution development & analysis:
- Support AI Scientists in building, configuring, and testing applied AI/GenAI solutions for high-value banking use cases such as credit memo generation, policy/regulatory summarization, knowledge assistants, and internal productivity copilots.
- Develop and refine prompts, grounding instructions, structured outputs, evaluation datasets, and Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector search, document chunking, and metadata-driven retrieval.
- Prepare and transform structured and unstructured data; assist with API integrations, notebooks, and repeatable workflows that improve quality, traceability, and analyst productivity.
Agentic workflow design & orchestration:
- Assist in designing and testing agentic workflows using approved enterprise platforms and frameworks, including multi-agent patterns, human-in-the-loop approvals, handoffs, and subagent / agents-as-tools designs.
- Work with emerging interoperability patterns such as Model Context Protocol (MCP), tool / skill / plugin integration, and Agent-to-Agent (A2A) connectivity under supervision and within approved guardrails.
- Document workflow steps, tool schemas, fallback logic, escalation paths, and safe disablement / rollback procedures for agentic and GenAI solutions.
Microsoft Foundry, Copilot, and enterprise platform enablement:
- Support prototyping and evaluation in Microsoft Foundry / Foundry Agent Service and related Microsoft ecosystems (for example, Microsoft 365 Copilot) while partnering with Engineering and Data teams operating on Snowflake and other approved enterprise platforms.
- Assist with prompt and agent configuration, retrieval integration, logging, model / tool versioning, and operational runbooks.
Evaluation, testing, and monitoring:
- Create and execute test cases for accuracy, groundedness, hallucination, task completion, retrieval quality, bias/fairness, and basic adversarial or prompt-injection scenarios; escalate issues promptly.
- Track KPIs/KRIs, output quality, drift indicators, and user feedback; maintain evidence needed for pilot reviews, production monitoring, and periodic reassessment.
AI governance, intake documentation, and cybersecurity/privacy controls:
- Support AI use-case intake and governance processes by preparing required documentation (e.g., AI intake forms, model/system inventories, change-control artifacts, and audit-ready evidence packs) aligned to MCB’s Trustworthy & Responsible AI Principles and internal approval processes.
- Learn domain and process context for new use cases by speaking with business owners and control partners; translate requirements into clear problem statements, data needs, user journeys, control gates, and documentation required to comply with internal and external regulations.
- Support third-party / AI vendor due diligence by collecting and organizing evidence, reviewing vendor documentation, and helping risk-tier AI vendors and solutions in partnership with Third-Party Risk and Cyber/IT; act as an AI subject matter expert in providing an understanding of AI-specific risks.
- Apply privacy-by-design, data minimization, access-control, and secure prompt/data handling practices; follow approved tooling and build standards.
Cross-functional partnership and continuous learning:
- Communicate findings, limitations, and recommendations clearly to AI Scientists, managers, control partners (Model Risk, Compliance/Legal, Cyber/IT, Data Privacy), and business stakeholders; incorporate feedback quickly and accurately.
- Contribute to playbooks, standard operating procedures, and reusable templates for prompts, evaluations, agent patterns, and workflow controls.
Innovation and best practices:
- Stay current on practical applied-AI methods—including RAG, evaluation frameworks, agentic workflow design, MCP, A2A, and Microsoft Foundry capabilities—and recommend fit-for-purpose uses under established control gates.
- Demonstrate sound judgment, curiosity, and willingness to learn from senior AI Scientists while promoting responsible AI, reproducibility, and disciplined execution.
Qualifications & Skills:
- 2+ years of work experience.
- Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, Information Systems, Mathematics, or a related field is preferred. Relevant internships, apprenticeships, or comparable hands-on AI, analytics, or automation project experience will be considered.
- Working knowledge of Python, SQL, notebooks, REST APIs, JSON/YAML, and version control (e.g., Git) for data analysis and AI workflow support.
- Understanding of traditional machine learning model development and lifecycle concepts, including model training, data leakage prevention, testing/validation, monitoring, and common performance metrics for classification and regression.
- Familiarity with LLM application patterns, including prompt engineering, structured outputs, function/tool calling, Retrieval-Augmented Generation (RAG), embeddings, and vector search.
- Familiarity with Microsoft Foundry (including Foundry Agent Service), Microsoft 365 Copilot / Copilot Studio concepts, and approved enterprise data/AI platforms such as Snowflake.
- Understanding of agentic workflow concepts such as MCP servers/tools, skills/plugins, subagents or agents-as-tools, handoffs, human-in-the-loop controls, and A2A integration patterns.
- Ability to test and evaluate AI outputs for accuracy, groundedness, hallucination, retrieval quality, bias/fairness, and basic security misuse scenarios.
- Working knowledge of model and data governance concepts, including documentation, monitoring, change control, inventories, audit trails, and three-lines-of-defense oversight in a regulated environment.
- Strong written and verbal communication skills; ability to turn technical findings into clear summaries, test evidence, and action items for business and risk stakeholders.
- Analytical, organized, and adaptable mindset; ability to learn quickly, manage multiple workstreams, and balance innovation with risk discipline.
Preferred Qualifications & Skills
- Financial services exposure in fraud, AML/KYC/CDD/EDD, underwriting, commercial banking, treasury, policy governance, or contact center analytics.
- Hands-on experience with Microsoft Foundry / Foundry Agent Service, Azure AI Search or equivalent retrieval tooling, M365 Copilot / Copilot Studio, Snowflake, or similar enterprise AI platforms is a plus.
- Experience with RAG architectures, vector databases / vector search, document chunking, metadata extraction, OCR/document understanding, and prompt engineering.
- Exposure to agentic workflow patterns such as MCP tool integration, skills/plugins, handoffs, subagents, A2A, and evaluation frameworks for task completion and tool-call quality.
- Familiarity with LangChain, Semantic Kernel, LangGraph, Microsoft Agent Framework, or similar orchestration libraries/frameworks is a plus.
- Awareness of external regulations including SR 11-7, SR 23-4, AI use-case intake / inventory processes, privacy, cybersecurity, and third-party risk expectations in a regulated environment.
- Ability to work in a constantly evolving environment.
- Must have excellent written and verbal communication skills.
- Demonstrate analytical, troubleshooting, and problem-solving skills.
- The ability to learn new technologies quickly.
- Self-directed individual with strong organizational and communication skills.
- Ability to synthesize multiple sources of information with an understanding of the bigger picture needs and operations of the Bank.
- Collaborative team player who can find practical solutions in a dynamic work environment.
- Ability to handle ambiguity, manage multiple tasks at once, and shift effectively between priorities.
Potential Salary: $120,000 - $150,000 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.
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.
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Apply to Metropolitan Commercial BankAI Engineer
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Come Work With Us:
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 (the “Bank”) is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of the IT Application Development and Support team and working closely with the Chief Artificial Intelligence Officer, transforming innovative AI prototypes into robust, scalable production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities (e.g., generative AI systems, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with 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)
Essential Functions & Responsibilities
Production Architecture & AIOps
- Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services.
- Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management.
- Design and maintain scalable software applications with integrated AI/ML capabilities.
- Develop software architecture and design patterns to ensure performance and scalability.
- Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
- Implement and manage data pipelines for preprocessing and transforming data for AI/ML models.
- Integrate AI/ML models into production environments and optimize for reliability and scalability.
- Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions.
- Conduct code reviews and provide technical guidance to junior developers.
- Stay current with advancements in software engineering and AI/ML technologies.
- Adhere to agile and lean software development best practices.
- Thoroughly document all developed models and processes according to relevant policies and standards.
- Support the production environment by either resolving technical or functional issues, in line with the procedures defined by the Bank.
Cross-Functional Collaboration
- Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms.
- Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback.
Scaling & Performance
- Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability.
- Optimize model serving using techniques like model compression, caching, and hardware acceleration.
Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues).
Qualifications & Skills:
- 6+ years of experience
- Master’s or PhD in a relevant field (Computer Science, Software Engineering, Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML
- LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using RAG, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures.
- MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow).
- Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering.
- Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
- Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams).
- Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets.
- Strong understanding of AI/ML algorithms, application architecture, and design patterns.
- Excellent problem-solving, analytical, communication, and collaboration skills.
Preferred Qualifications & Skills
- Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
- Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling.
- Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams.
- Knowledge of SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
- Ability to work in a constantly evolving environment.
- Excellent written and verbal communication skills.
- Strong listening and teaching abilities.
- Demonstrated analytical, troubleshooting, and problem-solving skills.
- Quick learner of new technologies.
- Self-directed with strong technology and communication skills.
- Ability to synthesize multiple sources of information and understand the broader operational context of the Bank.
- Collaborative team player with a practical and creative approach in a dynamic work environment.
- Ability to handle ambiguity, multitask, and adapt quickly to changing priorities.
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.
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.
Ready to apply?
Apply to Metropolitan Commercial BankAI Scientist
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Come Work With Us:
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).
Standard 4-day in-office requirement, 1 day remote (of your choosing)
Essential Functions & Responsibilities
Applied AI/ML development:
- 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.
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 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.
Productionization & MLOps on Snowflake
- 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).
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 stacks.
Third‑party AI & data stewardship:
- 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.
Cross‑functional partnership:
- 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).
Innovation, coaching, and best practices:
- 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.
Qualifications & Skills:
- 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.
Preferred Qualifications & Skills
- 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).
- 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.
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.
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.
Ready to apply?
Apply to Metropolitan Commercial Bank