U.S. Bank
Data Scientist - AI/ML Model Development & Productization
U.S. Bank, Charlotte, North Carolina, United States, 28245
Apply for the
Data Scientist – AI/ML Model Development & Productization
role at
U.S. Bank .
Posted 3 days ago – be among the first 25 applicants.
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, feature engineering, experimentation to deployment, monitoring, and continuous improvement—while collaborating with peer data scientists, data engineers, product managers, and DevOps teams.
About the Role Your day‑to‑day will involve designing robust experiments, selecting appropriate algorithms, and validating models against rigorous metrics. You’ll translate models into production pipelines using containerization, orchestration, and MLOps tooling, ensuring reproducible, version‑controlled, compliant deployments. Post‑deployment, you’ll set up automated monitoring, drift detection, and A/B testing frameworks.
Beyond the technical stack, this position requires strong communication skills to translate model insights into actionable business recommendations and to partner with stakeholders and engineering teams to shape our AI platform and data strategy.
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 large language models (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).
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 assumptions, limitations, and results clearly, and provide documentation, workshops, and SDKs.
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 relevant experience in AI/ML.
Understanding of machine learning techniques and algorithms.
Strong proficiency in Python (NumPy, pandas, scikit‑learn, TensorFlow/PyTorch, Keras, Caffe).
Experience with large language models (LLMs) and generative AI workflows, including RAG building, VectorDB, prompt engineering, LLM serving, and familiarity with providers such as LLamaIndex, LangChain, LangGraph, Ollama, VLLM.
Familiarity with Transformers, NLP technology, and computer vision techniques and applications.
Hands‑on experience with containerization (Docker) and orchestration (Kubernetes) and CI/CD (GitHub Actions, GitLab CI, ArgoCD).
Familiarity with MLOps platforms (MLflow, Kubeflow, Airflow) and experiment tracking.
Hands‑on experience with data and logging environments such as 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 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 with a minimum of 3 in‑office days per week and the flexibility to work remotely for the other days.
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 (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, veteran status, or other factors protected under applicable law.
U.S. Bank participates in the U.S. Department of Homeland Security E‑Verify program in all U.S. facilities and certain U.S. territories.
Pay Range: $119,765.00 – $140,900.00.
Posting may be closed earlier due to high volume of applicants.
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.
#J-18808-Ljbffr
Data Scientist – AI/ML Model Development & Productization
role at
U.S. Bank .
Posted 3 days ago – be among the first 25 applicants.
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, feature engineering, experimentation to deployment, monitoring, and continuous improvement—while collaborating with peer data scientists, data engineers, product managers, and DevOps teams.
About the Role Your day‑to‑day will involve designing robust experiments, selecting appropriate algorithms, and validating models against rigorous metrics. You’ll translate models into production pipelines using containerization, orchestration, and MLOps tooling, ensuring reproducible, version‑controlled, compliant deployments. Post‑deployment, you’ll set up automated monitoring, drift detection, and A/B testing frameworks.
Beyond the technical stack, this position requires strong communication skills to translate model insights into actionable business recommendations and to partner with stakeholders and engineering teams to shape our AI platform and data strategy.
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 large language models (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).
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 assumptions, limitations, and results clearly, and provide documentation, workshops, and SDKs.
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 relevant experience in AI/ML.
Understanding of machine learning techniques and algorithms.
Strong proficiency in Python (NumPy, pandas, scikit‑learn, TensorFlow/PyTorch, Keras, Caffe).
Experience with large language models (LLMs) and generative AI workflows, including RAG building, VectorDB, prompt engineering, LLM serving, and familiarity with providers such as LLamaIndex, LangChain, LangGraph, Ollama, VLLM.
Familiarity with Transformers, NLP technology, and computer vision techniques and applications.
Hands‑on experience with containerization (Docker) and orchestration (Kubernetes) and CI/CD (GitHub Actions, GitLab CI, ArgoCD).
Familiarity with MLOps platforms (MLflow, Kubeflow, Airflow) and experiment tracking.
Hands‑on experience with data and logging environments such as 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 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 with a minimum of 3 in‑office days per week and the flexibility to work remotely for the other days.
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 (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, veteran status, or other factors protected under applicable law.
U.S. Bank participates in the U.S. Department of Homeland Security E‑Verify program in all U.S. facilities and certain U.S. territories.
Pay Range: $119,765.00 – $140,900.00.
Posting may be closed earlier due to high volume of applicants.
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.
#J-18808-Ljbffr