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Data Scientist With Sagemaker Jobs (NOW HIRING)

The Data Scientist II is expected to independently lead analyses and model development projects ... SageMaker, S3) and Snowflake. * Familiarity with MLOps tools and practices, including MLflow ...

Data Scientist Principal

WV · On-site +1

$144K - $195K/yr

Expertise in AI/ML cloud technologies (preferably on AWS, including Bedrock, Lambda, and SageMaker) * Experienced with Python data science stack (Pandas, NumPy, scikit-learn, PyTorch/TensorFlow) and ...

Senior Data Scientist

Chicago, IL · On-site

$140K - $180K/yr

Hands-on experience with cloud data science platforms such as Databricks, AWS SageMaker, Azure ML, Snowflake Snowpark, or Palantir Foundry. * Strong stakeholder management skills and the ability to ...

Sr. Data Scientist

Bellevue, WA · On-site

$140K - $180K/yr

Hands-on experience with cloud data science platforms such as Databricks, AWS SageMaker, Azure ML, Snowflake Snowpark, or Palantir Foundry. * Strong stakeholder management skills and the ability to ...

Data Scientist Lead

Tampa, FL · On-site

$170 - $230/hr

As Data Scientist Lead within Commercial & Investment Bank with the Healthcare Provider team, you ... AWS SageMaker/Bedrock, and hands‑on experience with CNN/transformer architectures, OCR ...

Data Scientist

Plano, TX · On-site

$62 - $67/hr

Familiarity with version control (Git) and CI/CD for model deployment * Comfort working in cloud ML environments (AWS SageMaker, Azure ML, or similar) * A track record of applying data science to ...

Showing results 41-60

Data Scientist With Sagemaker information

See salary details

$37.5K

$122.7K

$196.5K

How much do data scientist with sagemaker jobs pay per year?

As of Sep 3, 2026, the average yearly pay for data scientist with sagemaker in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a data scientist with SageMaker?

A Data Scientist with SageMaker is a professional who leverages Amazon SageMaker, a cloud-based machine learning platform, to build, train, and deploy machine learning models at scale. They are skilled in data analysis, statistical modeling, and using SageMaker's tools for tasks such as data preprocessing, model selection, and automated machine learning (AutoML). These data scientists streamline workflows by taking advantage of SageMaker's integrated Jupyter notebooks, managed training, and deployment services to deliver insights and predictive solutions efficiently.

What are the key skills and qualifications needed to thrive as a data scientist with SageMaker?

To thrive as a Data Scientist with SageMaker, you need strong skills in statistics, machine learning, programming (Python, R), and a solid background in data analysis, typically supported by a relevant degree. Mastery of AWS SageMaker, cloud platforms, version control tools, and certifications like AWS Certified Machine Learning are highly valued. Excellent problem-solving, communication, and the ability to work collaboratively set outstanding professionals apart in this role. These skills are crucial for building, deploying, and explaining scalable machine learning models that deliver real business value.

How does a data scientist with SageMaker typically collaborate with engineering and DevOps teams?

As a Data Scientist utilizing SageMaker, you will frequently collaborate with engineering and DevOps teams to ensure that your machine learning models are seamlessly integrated into production environments. This involves sharing model artifacts, working together on deployment pipelines, and optimizing cloud resource usage. Clear communication is essential, as you'll need to explain model requirements and performance metrics to technical stakeholders. Collaboration often includes conducting code reviews, troubleshooting deployment issues, and participating in discussions about scalability and security within AWS infrastructure.

What is the difference between Data Scientist With Sagemaker vs Data Scientist?

AspectData Scientist With SagemakerData Scientist
Required SkillsMachine learning, AWS Sagemaker, Python, data analysisData analysis, machine learning, Python, R, SQL
Work EnvironmentCloud-based platforms, AWS ecosystemOn-premises or cloud, various platforms
CertificationsAWS certifications beneficialData science certifications (e.g., CAP, DASCA)
Industry UsageTech, finance, healthcare using AWSBroad across industries

While both roles involve data analysis and machine learning, Data Scientist With Sagemaker specializes in deploying models using AWS Sagemaker, focusing on cloud-based solutions. In contrast, Data Scientist roles are broader, covering various tools and platforms. The Sagemaker role emphasizes cloud skills and AWS certifications, making it ideal for cloud-centric organizations.

More about Data Scientist With Sagemaker jobs

What cities are hiring for Data Scientist With Sagemaker jobs?

Cities with the most Data Scientist With Sagemaker job openings:

What states have the most Data Scientist With Sagemaker jobs?

States with the most job openings for Data Scientist With Sagemaker jobs include:

Infographic showing various Data Scientist With Sagemaker job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Data Scientist II

Swbc

San Antonio, TX

Full-time

Medical, Retirement

Re-posted 7 days ago


SWBC rating

6.5

Company rating: 6.5 out of 10

Based on 17 frontline employees who took The Breakroom Quiz


Job description

SWBC is seeking a talented individual who will contribute to the development of machine learning models, analytical solutions, and AI-driven capabilities that directly impact business performance and client outcomes. This role works closely with the Senior Data Scientist, Analytics Engineers, and Data Analysts to design, build, and deploy data science solutions within SWBC's modern, cloud-native data ecosystem and enterprise AI/ML platform. The Data Scientist II is expected to independently lead analyses and model development projects with minimal guidance, while contributing to the broader AI strategy of the organization.

Why you'll love this role:

In this role, you will work hands-on with cutting-edge tools and platforms every day, including Hex for experimentation, Omni for AI context development powering Clara, and SWBC Intelligence our multi-model AI orchestration platform spanning AWS Bedrock and AWS Sagemaker to build and deploy intelligent solutions for internal and client-facing use cases. We offer a collaborative environment that values continuous learning, empirical rigor, and professional growth.

Essential duties include the following:

  • Design, develop, and deploy machine learning models and analytical solutions to address business problems, including forecasting, segmentation, classification, and anomaly detection.
  • Contribute to the development and enhancement of Clara, SWBC's AI decision assistant, including supporting context engineering within Omni, prompt development, and model evaluation workflows.
  • Build and maintain ML/AI experiment workflows using Hex for prototyping and exploration, and AWS SageMaker and MLflow for experiment tracking, feature engineering, and model versioning.
  • Develop and deploy AI/ML solutions within SWBC Intelligence, the enterprise multi-model AI orchestration platform, leveraging AWS Bedrock and AWS Sagemaker as directed by senior team members.
  • Conduct exploratory data analysis and feature engineering using data sourced from SWBC's governed medallion architecture (Bronze - Silver - Gold) in Snowflake.
  • Participate in the platform's Evaluation Harness process, including test case development, model benchmarking, and scoring framework contribution to ensure solution quality.
  • Perform AI experimentation and prototyping within governed sandbox environments, including Snowflake Sandbox and Hex, ensuring adherence to data privacy and compliance requirements.
  • Apply Responsible AI principles, including bias detection, model explainability, and fairness monitoring, to all model development activities.
  • Collaborate with cross-functional teams including Analytics Engineering, Data Management, and business stakeholders to translate business problems into data science solutions.
  • Communicate findings, model results, and recommendations to both technical and non-technical audiences through clear documentation, visualizations, and presentations.
  • Stay current with emerging trends in data science, machine learning, and AI, and contribute to the team's knowledge sharing and continuous improvement culture.

Serious candidates will possess the minimum qualifications:

  • Master's degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or a related discipline. Equivalent professional experience (5+ years) may be considered in lieu of an advanced degree.
  • Minimum of two (2) years of progressive experience in data science, analytics, or machine learning roles.
  • Proficiency in Python and SQL, with working knowledge of ML frameworks such as TensorFlow, PyTorch, scikit-learn, or equivalent.
  • Experience developing and deploying models within cloud-based AI/ML platforms, preferably AWS (SageMaker, S3) and Snowflake.
  • Familiarity with MLOps tools and practices, including MLflow, experiment tracking, and model versioning.
  • Proficiency in Hex for data science experimentation and prototyping.
  • Understanding of statistical modeling, hypothesis testing, and experimental design.
  • Ability to work with structured and semi-structured data, perform data cleaning, and conduct exploratory analysis.
  • Strong communication skills with the ability to present findings to both technical and non-technical audiences.
  • Ability to interpret ambiguity and work with minimal direction on defined project scopes.
  • Ability to lift 20 lbs. of files, supplies, documents, or other related items.

SWBC offers*:

  • Competitive overall compensation package
  • Work/Life balance
  • Employee engagement activities and recognition awards
  • Years of Service awards
  • Career enhancement and growth opportunities
  • Leadership Academy and Mentor Program
  • Continuing education and career certifications
  • Variety of healthcare coverage options
  • Traditional and Roth 401(k) retirement plans
  • Lucrative Wellness Program

*Based upon employee eligibility

Additional Information:

SWBC is a Substance-Free Workplace and requires pre-employment drug testing.

Please note, SWBC does not hire tobacco users as allowed by law.

To learn more about SWBC, visit our website at www.SWBC.com. If interested, please click the appropriate apply button.


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