Data Scientist - Applied AI Scientist

Zions Bancorporation's Enterprise Technology and Operations (ETO) team is transforming what it means to work for a financial institution. With a commitment to technology and innovation, we have been providing our community, clients and colleagues the best experience possible for over 150 years. Help us transform our workforce of the future, today.

Zions Bancorporation's Innovation Lab is seeking a creative and driven Data Scientist (Applied AI Scientist) who bridges the gap between rigorous statistical research and production-grade software engineering. This role is at the heart of our innovation engine. You will not only uncover deep data insights and design advanced AI algorithms, but you will also architect the robust, scalable code required to bring those concepts to life.

As a key member of the Innovation Lab, you will work in a fast-paced, experimental environment, turning ambiguous business challenges into tangible, data-driven prototypes. We need a scientist who treats machine learning as an engineering discipline, someone who understands the "why" behind the math, and the "how" of robust software implementation.

Visa Sponsorship:
This Data Scientist position is currently NOT eligible for employment visa sponsorship (e.g., H-1B visa). This includes, for example, situations where a candidate may have temporary work authorization while enrolled in school or upon graduation (e.g., CPT, OPT) but would need H-1B visa sponsorship within a few years of employment in order to maintain employment eligibility.

Responsibilities:

• End-to-End AI Design: Design, prototype, and validate ML/AI solutions, translating complex business challenges into mathematical formulations and scalable, production-ready code.
• Advanced Analytics & EDA: Perform deep exploratory data analysis, statistical testing, and data transformations on diverse datasets (structured and unstructured) to uncover predictive signals and validate hypotheses.
• Production-Grade Science: Architect and implement modular, extensible, and testable Python codebases for AI experiments. Move beyond Jupyter notebooks by applying clean-code principles (SOLID, DRY) for seamless hand-off to ETO Engineering teams.
• Agentic & Generative AI: Develop and experiment with applied generative AI and multi-agent architectures using orchestration frameworks (e.g., Lang Chain, Lang Graph), focusing on optimal state management, robust RAG pipelines, and efficient system design.
• Algorithmic Optimization: Optimize model inference, data processing pipelines, and memory footprints for latency and scalability, applying a strong understanding of data structures and algorithmic complexity.
• Rigorous Evaluation: Build automated evaluation frameworks to benchmark model performance, mitigate hallucinations, track drift, and ensure algorithmic fairness via A/B testing and statistical rigor.
• Collaboration & Communication: Act as the technical translator between research-focused ideation and engineering execution. Communicate complex statistical findings and system architectures to both technical and non-technical stakeholders.

Qualifications:

• The Scientist's Mind: Solid foundation in statistics (Bayesian/Frequentist), linear algebra, hypothesis testing, and the internal mechanics of ML algorithms (e.g., how optimizers work, loss functions, attention mechanisms).
• The Engineer's Toolbelt: Advanced Python proficiency with a strong focus on Object-Oriented Programming (OOP) and modular design. You must be comfortable writing unit tests (e.g., Pytest) for your data pipelines and models.
• Framework Depth: Deep expertise with ML libraries (PyTorch, Tensor Flow, Scikit-learn, Pandas) and experience implementing custom logic, rather than just calling out-of-the-box models.
• Generative AI Systems: Hands-on experience with NLP, Large Language Models (LLMs), and Vector Databases, with an understanding of how to evaluate and optimize these systems at scale.
• Software Maturity: Proficiency with Git/version control, containerization (Docker), API development (FastAPI/Flask), and a working knowledge of how models fit into a CI/CD lifecycle (MLOps).
• Problem Solving: Exceptional problem-solving skills, comfort with ambiguity, and the ability to own the data science lifecycle from abstract ideation to engineered prototype.
• Education & Experience: Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field plus 4+ years of hands-on experience in applied machine learning or data science. A Master's degree or PhD is a plus. A combination of education and experience may meet qualifications.

Location:

This position has a hybrid work from home schedule with a minimum of three days per week in the office at the new Zions Technology Center in Midvale, UT.

The Zions Technology Center is a 400,000-square-foot technology campus in Midvale, Utah. Located on the former Sharon Steel Mill superfund site, the sustainably built campus is the company's primary technology and operations center. This…