Lead AI Data Scientist — ML, MLOps & Risk Modeling

Jobtailor; Arizona; On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor is seeking a senior data scientist to design and implement predictive analytics and ML models. You will work with structured and unstructured data to build scalable solutions, deploy models within risk management frameworks, and document technical approaches for stakeholders.

You will mentor junior data scientists, collaborate with data engineering and IT, and stay current with AI methodologies and regulatory considerations.

Qualifications

• Requires bachelor’s in a quantitative field or 4 years of related education/experience with 6+ years in predictive analytics.
• Advanced degree plus 4+ years of predictive analytics experience is preferred.
• 4+ years training and validating statistical/ML models.
• 4+ years using Python for analysis and ML modeling.
• Experience writing clean, documented code.

Responsibilities

• Gather, interpret, and manipulate structured and unstructured data to enable analytical solutions.
• Develop scalable, automated ML/AI solutions.
• Select modeling techniques based on data limits and business needs.
• Develop and deploy models within risk management frameworks.
• Mentor junior data scientists and share knowledge.

Skills

Predictive Analytics

Machine Learning Model Development

Python for Statistical Analysis

SQL Querying and Preprocessing

MLOps Integration in AWS or GCP

Education

Bachelor's degree in a quantitative field

Advanced degree in mathematics/CS/stats/engineering or AI

Tools

LangChain

LangGraph

AgentCore

VertexAI

AWS

Job description

Jobtailor is seeking a senior data scientist to design and implement predictive analytics and ML models. You will work with structured and unstructured data to build scalable solutions, deploy models within risk management frameworks, and document technical approaches for stakeholders.

You will mentor junior data scientists, collaborate with data engineering and IT, and stay current with AI methodologies and regulatory considerations.