Data Science and Analytics Engineer

Madison-Davis; New York (NY); Presencial; USD 150.000 - 230.000; Jornada completa

Descripción de la vacante

Madison-Davis, LLC seeks a senior Data Science and Analytics Engineer to expand enterprise AI capabilities and operationalize ML at scale in a leading financial services environment. You will design production-grade ML solutions, own end-to-end analytics delivery, and guide engineering, governance, and deployment practices while remaining hands-on in development.

In this hands-on leadership role, you will mentor teams, set standards for ML lifecycle, and collaborate with risk, compliance, and

Formación

• 10+ years of experience across data science, AI/ML, analytics, or related fields.
• Deep hands-on Python and SQL experience.
• Strong ML, predictive modeling, and statistical analytics background.
• Proven track record delivering ML/AI in production.
• Databricks, Spark, and cloud-analytics experience.
• Experience with modern ML frameworks and lifecycle tooling.
• MLOps experience including deployment, CI/CD, monitoring.
• Experience in financial services or regulated environments.
• Strong communication and stakeholder-management skills.
• Ability to provide technical leadership while remaining hands-on in development.

Responsabilidades

• Design, develop, and deploy enterprise machine-learning, AI, and advanced-analytics solutions.
• Own analytics delivery from problem definition and feature engineering through model development, deployment, monitoring, and adoption.
• Build scalable ML pipelines using Python, Databricks, Spark, and cloud-native technologies.
• Integrate models into enterprise applications and operational workflows.
• Establish MLOps standards for deployment, CI/CD, experiment tracking, model monitoring, and lifecycle management.
• Support model explainability, validation, governance, and auditability.
• Partner with business, engineering, risk, compliance, data, and operations teams.
• Provide technical leadership while remaining directly involved in development and implementation.

Conocimientos

Python

SQL

Machine learning

Databricks

Spark

MLOps

Cloud analytics

Stakeholder management

Technical leadership

Herramientas

Azure ML

MLflow

Descripción del empleo

A large financial institution is seeking a senior Data Science and Analytics Engineer to help expand and operationalize enterprise AI and machine-learning capabilities. This is a highly hands‑on position for someone who combines deep technical expertise with the ability to provide direction, establish standards, and communicate effectively with business and technology leadership. You will build production‑grade AI and analytics solutions while helping mature the organization s engineering, MLOps, governance, and deployment practices.

RESPONSIBILITIES

• Design, develop, and deploy enterprise machine‑learning, AI, and advanced‑analytics solutions.
• Own analytics delivery from problem definition and feature engineering through model development, deployment, monitoring, and adoption.
• Build scalable ML pipelines using Python, Databricks, Spark, and cloud‑native technologies.
• Integrate models into enterprise applications and operational workflows.
• Establish MLOps standards for deployment, CI/CD, experiment tracking, model monitoring, and lifecycle management.
• Support model explainability, validation, governance, and auditability.
• Partner with business, engineering, risk, compliance, data, and operations teams.
• Provide technical leadership while remaining directly involved in development and implementation.

Role Requirements

• 10+ years of experience across data science, AI/ML engineering, advanced analytics, quantitative modeling, or related areas.
• Deep hands‑on Python and SQL experience.
• Strong machine‑learning, predictive‑modeling, and statistical‑analytics background.
• Demonstrated experience delivering ML or AI solutions into production.
• Databricks, Spark, and cloud‑analytics experience.
• Experience with modern ML frameworks and model‑lifecycle tooling.
• Practical MLOps experience including deployment, CI/CD, monitoring, and experiment tracking.
• Experience within financial services or another regulated enterprise environment.
• Strong communication and stakeholder‑management skills.
• Ability to provide technical leadership without moving away from hands‑on execution.

PREFERRED EXPERIENCE

• Banking, lending, payments, fraud, AML, risk, or regulatory‑analytics experience.
• Production GenAI, LLM, NLP, or intelligent‑automation experience.
• Azure ML and MLflow.
• Model‑risk or AI‑governance experience.
• Feature stores, vector retrieval, RAG, or real‑time inference architectures.