U.S. Bank
Data Scientist - AI/ML Model Development & Productization
U.S. Bank, Chicago, Illinois, United States, 60290
Data Scientist – AI/ML Model Development & Productization at U.S. Bank.
Job Description 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 development and experimentation to deployment, monitoring, and continuous improvement—while collaborating with peer data scientists, data engineers, product managers, and DevOps to deliver scalable, high‑quality AI services.
Core responsibilities include designing robust experiments, selecting appropriate algorithms, validating models against rigorous metrics, translating models into production pipelines with containerization and orchestration, setting up automated monitoring, drift detection, and A/B testing, and ensuring compliance with governance standards.
Core Responsibilities
Build, prototype, and refine ML models that solve core business problems, from feature engineering to end‑to‑end deployment.
Engineer scalable feature pipelines and maintain feature stores for training and inference.
Package models (Docker, Kubernetes, SageMaker, etc.) and create CI/CD pipelines with GitHub Actions, GitLab CI, Jenkins, using MLOps tools such as MLflow, Kubeflow, and Airflow.
Deploy, fine‑tune, prompt‑engineer, and scale large language models and generative AI services, ensuring robust inference performance.
Implement real‑time monitoring, logging, alerting, audit trails, RBAC, and compliance controls (GDPR, HIPAA) to maintain model integrity and regulatory adherence.
Own the full model lifecycle: test, ship, monitor for drift, and trigger automated retraining workflows.
Translate product requirements into data‑driven solutions, communicate model assumptions and results clearly, and provide documentation, workshops, and SDKs to empower teams.
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 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, generative AI workflows, RAG building, VectorDB, prompt engineering, and LLM serving.
Familiarity with Transformers, NLP technology and other CV techniques.
Hands‑on experience with containerization, orchestration, and CI/CD (Docker, Kubernetes, GitHub Actions, GitLab CI, ArgoCD).
Familiarity with MLOps platforms (MLflow, Kubeflow, Airflow) and experiment tracking.
Experience with data and log aggregation environments (Elasticsearch, MongoDB, Cassandra).
Knowledge of model monitoring, drift detection, and automated retraining pipelines.
Exposure to cloud services (AWS, GCP, Azure) and their AI/ML 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 communication skills; 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.
Hybrid/flexible schedule: expected in‑office 3+ days per week; remaining days remote.
Benefits
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 (2–5 weeks depending on grade and tenure)
Up to 11 paid holiday opportunities
Adoption assistance
Sick and safe leave accruals of 1 hour per 30 worked, up to 80 hours per calendar year
E‑Verify: U.S. Bank participates in the U.S. Department of Homeland Security E‑Verify program in all facilities in the United States and certain territories.
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.
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Job Description 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 development and experimentation to deployment, monitoring, and continuous improvement—while collaborating with peer data scientists, data engineers, product managers, and DevOps to deliver scalable, high‑quality AI services.
Core responsibilities include designing robust experiments, selecting appropriate algorithms, validating models against rigorous metrics, translating models into production pipelines with containerization and orchestration, setting up automated monitoring, drift detection, and A/B testing, and ensuring compliance with governance standards.
Core Responsibilities
Build, prototype, and refine ML models that solve core business problems, from feature engineering to end‑to‑end deployment.
Engineer scalable feature pipelines and maintain feature stores for training and inference.
Package models (Docker, Kubernetes, SageMaker, etc.) and create CI/CD pipelines with GitHub Actions, GitLab CI, Jenkins, using MLOps tools such as MLflow, Kubeflow, and Airflow.
Deploy, fine‑tune, prompt‑engineer, and scale large language models and generative AI services, ensuring robust inference performance.
Implement real‑time monitoring, logging, alerting, audit trails, RBAC, and compliance controls (GDPR, HIPAA) to maintain model integrity and regulatory adherence.
Own the full model lifecycle: test, ship, monitor for drift, and trigger automated retraining workflows.
Translate product requirements into data‑driven solutions, communicate model assumptions and results clearly, and provide documentation, workshops, and SDKs to empower teams.
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 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, generative AI workflows, RAG building, VectorDB, prompt engineering, and LLM serving.
Familiarity with Transformers, NLP technology and other CV techniques.
Hands‑on experience with containerization, orchestration, and CI/CD (Docker, Kubernetes, GitHub Actions, GitLab CI, ArgoCD).
Familiarity with MLOps platforms (MLflow, Kubeflow, Airflow) and experiment tracking.
Experience with data and log aggregation environments (Elasticsearch, MongoDB, Cassandra).
Knowledge of model monitoring, drift detection, and automated retraining pipelines.
Exposure to cloud services (AWS, GCP, Azure) and their AI/ML 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 communication skills; 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.
Hybrid/flexible schedule: expected in‑office 3+ days per week; remaining days remote.
Benefits
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 (2–5 weeks depending on grade and tenure)
Up to 11 paid holiday opportunities
Adoption assistance
Sick and safe leave accruals of 1 hour per 30 worked, up to 80 hours per calendar year
E‑Verify: U.S. Bank participates in the U.S. Department of Homeland Security E‑Verify program in all facilities in the United States and certain territories.
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.
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