Applied Machine Learning Scientist II – AI/ML, Model Validation, GenAI, Agentic AI

. Lead end-to-end development and deployment of advanced AI/ML solutions for model validation and operational domains .

Posted 9/15/2026full-timeNew York City • New York • United StatesJunior💰 $96,130 - $155,950 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying advanced AI/ML solutions, including proficiency in Python, PySpark, and cloud-based platforms like Azure Databricks. Capable of leading technical architecture discussions and ensuring model governance and responsible AI practices.

Highest-signal resume keywords

AI/ML Solution DevelopmentPython ProgrammingDeep Learning ExpertiseModel ValidationAI Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningNatural Language ProcessingGenerative AIStatistical ModelingAI Application ArchitectureData Science PipelinesSQLPrompt EngineeringModel Risk Management

Soft Skills

Excellent CommunicationMentoringCross-Functional InfluenceResearch Mindset

Tools & Technologies

Azure DatabricksPySparkMLflowGitCI/CDTensorFlowPyTorchLangChainHugging FaceVector Databases

Industry Keywords

FraudFinancial ServicesRisk ManagementPaymentsRegulatory Compliance

Tech Stack

Tools & technologies

AzureCloudPySparkPythonPyTorchSQLTensorflow

About the role

Key responsibilities & impact

Lead end-to-end development and deployment of advanced AI/ML solutions for model validation and operational domains
Design and implement production-grade machine learning systems using statistical modeling, deep learning, Generative AI, NLP, graph analytics, and Agentic AI frameworks
Develop LLM-powered applications, AI copilots, agentic workflows, RAG solutions, multi-agent orchestration frameworks, intelligent decision support systems, and human-in-the-loop AI solutions
Develop scalable data science and AI pipelines using Python, Databricks, Azure, PySpark, MLflow, vector databases, orchestration frameworks, and modern AI tooling
Partner with business leaders, fraud strategy, engineering, MLOps, governance, and enterprise AI teams
Translate ambiguous business problems into analytical frameworks, technical solutions, and actionable insights
Lead technical architecture discussions and contribute to AI platform strategy, solution design, and enterprise AI standards
Communicate analytical concepts and AI solution designs to executives, stakeholders, and governance partners
Ensure model governance, explainability, monitoring, and responsible AI practices throughout the AI/ML lifecycle
Mentor and guide junior scientists
Monitor emerging industry trends, academic research, and AI technologies
Deliver insights, lead iterative learning cycles, and create analytic solutions that become core deliverables
Design and deliver enterprise analytic solutions and develop business insights from broad data sources
Conduct research, analysis, presentations, risk assessments, remediation planning, and continuous process improvements
Manage relationships across business lines and control functions while maintaining regulatory alignment
Participate as a team member, share knowledge, support team development, and act as a bank brand ambassador

Requirements

What you’ll need

Undergraduate degree required
1+ years relevant experience, including post graduate experience
Advanced technical degree preferred in math, physics, engineering, finance, computer science, or related quantitative discipline
Extensive experience developing and deploying advanced AI/ML solutions in enterprise environments
Solid model validation background
Hands-on experience with machine learning, deep learning, NLP, LLMs, Generative AI, and modern AI application architectures
Experience with Agentic AI systems, AI copilots, Retrieval-Augmented Generation (RAG), prompt engineering frameworks, multi-agent workflows, conversational AI solutions, and knowledge retrieval systems
Deep expertise in Python, PySpark, SQL, and modern ML/AI frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, Hugging Face, and MLflow, or equivalent ecosystems
Experience with cloud-based AI/ML platforms such as Azure Databricks and distributed computing environments
Strong software engineering and productionization skills, including Git-based development workflows, CI/CD concepts, API integration, and scalable AI solution deployment
Experience developing AI/ML solutions within fraud, financial services, risk, payments, or highly regulated industries strongly preferred
Strong understanding of AI governance, explainability, model risk management, and responsible AI principles
Ability to lead complex initiatives and influence cross-functional stakeholders
Excellent communication and presentation skills
Ability to mentor junior team members
Strong research mindset and ability to evaluate and operationalize emerging AI techniques
Ability to perform sedentary work, operate standard office equipment, sit continuously, and concentrate for long periods
Occasional domestic travel

Benefits

Comp & perks

Variable compensation/incentive awards, including eligibility for cash and/or equity incentive awards
Health and well-being benefits
Savings and retirement programs
Paid time off, including Vacation PTO, Flex PTO, and Holiday PTO
Banking benefits and discounts
Career development
Reward and recognition
Regular career, development, and performance conversations with manager
Online learning platform
Mentoring programs
Training and onboarding sessions
Occasional domestic travel