Data Scientist - AI/ML Model Development & Productization

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, Mong