Senior Data Scientist — AI/ML for Scalable Analytics

NCino, Inc.; Lehi, Northern (UT, KY); Hybrid; USD 108,000 - 183,000; Full time

Job summary

nCino is transforming financial services with bold innovation and relentless execution. We seek a data science leader to develop, deploy, and monitor large-scale ML models, translate business questions into analytics projects, and drive value across the product lifecycle.

You will work with Python/R, leverage Databricks and MLflow, and collaborate across teams to ensure scalable, robust model development and deployment in production environments.

Qualifications

• Undergraduate degree in Data Science, Statistics, Mathematics, Computer Science or a related field with 5+ years of related experience.
• Proficiency in Python or R.
• Experience with data visualization tools (DOMO, AWS Quicksight, Tableau).
• Demonstrated mastery of machine learning algorithms and techniques.
• Strong problem-solving skills; ability to work independently and collaboratively.
• Experience deploying and monitoring models in production.

Responsibilities

• Develop, deploy, and monitor large-scale machine learning models.
• Lead and guide teams in implementing programming best practices and adopting emerging technologies.
• Apply AI insights to identify patterns and automate repetitive tasks to improve development efficiency and product quality.
• Translate business questions into analytics projects and deliver actionable insights.

Skills

Python

R

SQL

NoSQL

Databricks

MLflow

AWS

Agile

Data visualization

Education

Bachelor's degree in Data Science

Advanced degree in Data Science

Tools

Databricks

MLflow

DOMO

Quicksight

Tableau

SageMaker

Lambda

S3

Athena

Job description

nCino is transforming financial services with bold innovation and relentless execution. We seek a data science leader to develop, deploy, and monitor large-scale ML models, translate business questions into analytics projects, and drive value across the product lifecycle.

You will work with Python/R, leverage Databricks and MLflow, and collaborate across teams to ensure scalable, robust model development and deployment in production environments.