At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.

Job Description

About the Role

We’re looking for a hands‑on Data Scientist who thrives on turning complex business problems into production‑ready AI/ML solutions. In this role, you’ll own the end‑to‑end lifecycle of models—from model development, feature engineering and experimentation to deployment, monitoring, and continuous improvement—while collaborating with peer data scientist, data engineers, product managers, and DevOps to deliver scalable, high‑quality AI services.

Your day-to-day will involve designing robust experiments, selecting appropriate algorithms, and validating models against rigorous metrics. You’ll then translate these models into production pipelines using containerization, orchestration, and MLOps tooling, ensuring that deployments are reproducible, version‑controlled, and compliant with our governance standards. Post‑deployment, you’ll set up automated monitoring, drift detection, and A/B testing frameworks to guarantee that models maintain performance and fairness over time.

Beyond the technical stack, this position requires strong communication skills to translate model insights into actionable business recommendations. You’ll partner with product stakeholders to prioritize features, and with data engineering teams to optimize data pipelines and feature stores. Your contributions will shape the architecture of our AI platform, influence our data strategy, and help us maintain a competitive edge through reliable, high‑impact machine learning solutions.

Core Responsibilities

Model Design & Iteration

Build, prototype, and refine ML models that solve core business problems, from feature engineering to end‑to‑end deployment.

Feature Pipelines & Data Management

Engineer scalable feature pipelines, maintain a feature store for training and inference, and manage vector/feature databases for retrieval‑augmented generation (RAG) and LLMs.

Deployment & MLOps

Package models (Docker, Kubernetes, SageMaker, etc.), create CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins), and orchestrate automated deployments with MLOps tools such as MLflow, Kubeflow, and Airflow.

Generative‑AI Enablement

Deploy, fine‑tune, prompt‑engineer, and scale large language models (LLMs) and other generative AI services, ensuring robust inference performance.

Observability, Governance & Compliance

Implement real‑time monitoring, logging (ELK stack), alerting, audit trails, RBAC, and compliance controls (GDPR, HIPAA) to maintain model integrity and regulatory adherence.

Lifecycle Management

Own the full model lifecycle: model, package, test, ship, monitor for drift, and trigger automated retraining workflows.

Cross‑Functional Collaboration

Translate product requirements into data‑driven solutions, communicate model assumptions, limitations, and results clearly to stakeholders, and provide documentation, workshops, and SDKs to empower data scientists and product teams.

Continuous Learning & Innovation

Stay current with cutting‑edge research and integrate state‑of‑the‑art AI/ML techniques whenever they add business value.

Preferred Skills / Experience

- Master’s in Computer Science, Electrical Engineering, Data Science, or a related field.

- 6-8 years of years of relevant experience in AI/ML.

- Understanding of Machine Learning techniques and algorithms.

- Strong proficiency in Python (NumPy, pandas, scikit‑learn, TensorFlow/PyTorch, Keras, Caffe).

- Working experience with large language models (LLMs) and generative AI workflows to include RAG building, VectorDB, prompt engineering, LLM serving and understanding of LLM providers (LLamaIndex, Langchain, Langraph, Ollama, VLLM).

- Familiarity with Transformers, NLP technology and other CV techniques and applications.

- Hands‑on experience with containerization (Docker), orchestration (Kubernetes), and CI/CD (GitHub Actions, GitLab CI, ArgoCD).

- Familiarity with MLOps platforms (MLflow, Kubeflow, Airflow) and experiment tracking.

- Hands on experience with any of the data and log aggregation environment (Elasticsearch, Mongo DB, Cassandra).

- Knowledge of model monitoring, drift detection, and automated retraining pipelines.

- Exposure to cloud services (AWS, GCP, Azure) and their AI/ML service offerings.

- Proven track record of delivering end‑to‑end solutions that impacted key metrics.

- Experience with large‑scale data pipelines, feature stores, and data quality frameworks.

- Understanding of model interpretability, fairness, and compliance best practices.

- Excellent communicator, able to explain technical concepts to non‑technical stakeholders.

- Collaborative mindset, comfortable working in agile teams.

- Strong problem‑solving orientation and curiosity to experiment with new ideas.

**The role offers a hybrid/flexible schedule, which means there's an in-office expectation of 3 or more days per week and the flexibility to work outside the office location for the other days.**

If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.

Benefits:

Our approach to benefits and total rewards considers our team members’ whole selves and what may be needed to thrive in and outside work. That's why our benefits are designed to help you and your family boost your health, protect your financial security and give you peace of mind. Our benefits include the following (some may vary based on role, location or hours):

• Healthcare (medical, dental, vision)

• Basic term and optional term life insurance

• Short-term and long-term disability

• Pregnancy disability and parental leave

• 401(k) and employer-funded retirement plan

• Paid vacation (from two to five weeks depending on salary grade and tenure)

• Up to 11 paid holiday opportunities

• Adoption assistance

• Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law

U.S. Bank is an equal opportunity employer. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, and other factors protected under applicable law.

E-Verify

U.S. Bank participates in the U.S. Department of Homeland Security E-Verify program in all facilities located in the United States and certain U.S. territories. The E-Verify program is an Internet-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services. Learn more about the E-Verify program.

The salary range reflects figures based on the primary location, which is listed first. The actual range for the role may differ based on the location of the role. In addition to salary, U.S. Bank offers a comprehensive benefits package, including incentive and recognition programs, equity stock purchase 401(k) contribution and pension (all benefits are subject to eligibility requirements). Pay Range: $119,765.00 - $140,900.00

U.S. Bank will consider qualified applicants with arrest or conviction records for employment. U.S. Bank conducts background checks consistent with applicable local laws, including the Los Angeles County Fair Chance Ordinance and the California Fair Chance Act as well as the San Francisco Fair Chance Ordinance. U.S. Bank is subject to, and conducts background checks consistent with the requirements of Section 19 of the Federal Deposit Insurance Act (FDIA). In addition, certain positions may also be subject to the requirements of FINRA, NMLS registration, Reg Z, Reg G, OFAC, the NFA, the FCPA, the Bank Secrecy Act, the SAFE Act, and/or federal guidelines applicable to an agreement, such as those related to ethics, safety, or operational procedures.

Applicants must be able to comply with U.S. Bank policies and procedures including the Code of Ethics and Business Conduct and related workplace conduct and safety policies.

Posting may be closed earlier due to high volume of applicants.