Senior Data Scientist

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Hybrid

Quick summary

Work type: Hybrid
Location: Cary, NC
Employment: Full-time
Posted: 74 days ago
Freshness: Confirmed live today
Closes: Nov 30, 2026
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $158k

$98k $216k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $126,800–$189,187.

Based on 240 similar postings.

Employer

About Q2

Q2 Holdings is a cloud-based banking software company providing digital banking solutions to banks, credit unions, and alternative financial companies, including consumer and business banking platforms. Industry: Financial Technology & Digital Banking

Q2 currently has 43 open roles on FindRole.

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At a glance

TL;DR · Senior Data Scientist

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As a Senior Data Scientist, you will join the Relationship Pricing team to lead complex data science projects from initial exploration to production deployment. You will build and operate production systems that power analytics from large commercial banking datasets, specifically focusing on loan pricing, relationship profitability, and deal performance. Your daily work involves designing and prototyping machine learning models for pricing recommendations, deal win likelihood, and anomaly detection. You will also develop LLM-powered features using RAG pipelines, prompt engineering, and embedding-based retrieval. Required skills include proficiency in Python or R, SQL, and machine learning libraries, alongside experience with Git and LLM evaluation frameworks. You will translate commercial banking problems into technical solutions while mentoring junior team members and communicating insights through visualizations.

What does a Data Scientist earn?

Median $173200 from 338 postings across 66 companies.

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Skills

Python R SQL Machine Learning LLM RAG Prompt Engineering Git AWS GCP Azure Vector Databases PowerBI Data Science

What you'll do

Engineer features from large commercial banking datasets including loan pricing and relationship profitability.
Design and prototype machine learning models for pricing recommendations, deal win likelihood, and anomaly detection.
Build and evaluate LLM-powered features including RAG pipelines, prompt engineering, and embedding-based retrieval.
Lead complex data science projects from initial exploration through to production deployment.
Translate commercial banking problems into data science solutions and contribute to product roadmap prioritization.
Deploy and maintain production-grade models and analytics systems in collaboration with engineering teams.
Mentor junior team members on modeling best practices, experimentation, and responsible AI.
Communicate model insights and findings through visualizations and presentations to technical and non-technical audiences.

What we're looking for

Bachelor’s degree in Data Science, Computer Science, Statistics, or a relevant field and 8 years of related experience.
Advanced degree and 6+ years of related experience.
Equivalent related work experience.
Strong proficiency in Python or R, SQL, and machine learning libraries.
Hands-on experience with large language models including prompt engineering, RAG, and LLM evaluation frameworks.
Proven ability to lead end-to-end data science projects from discovery through production.
Experience writing clean, maintainable code and using version control like Git.
Fluent written and oral communication in English and authorization to work for any employer in the U.S.
Familiarity with vector databases and embedding-based retrieval (preferred).
Experience with cloud platforms such as AWS, GCP, or Azure (preferred).
Experience with BI tooling like PowerBI or equivalent (preferred).
Experience with financial services, banking, or fintech, with commercial banking familiarity strongly preferred (preferred).

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