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Data Scientist With Sagemaker Jobs (NOW HIRING)

Neptune

Data Scientist

Duluth, GA · On-site

Position Summary As a Data Scientist, you will be responsible for developing and implementing ... Experience with AWS services (SageMaker, Lambda, S3, Redshift). • Preferred: Experience with deep ...

Staffingine LLC

Data Scientist

San Francisco, CA · On-site

Contract * 50% - Consulting with internal teams (economists, analysts) to design and implement AI ... Implement solutions using AWS AI services (Bedrock, SageMaker, Textract, Databricks etc.) * Ensure ...

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SWBC

Data Scientist II

San Antonio, TX · On-site

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 ...

Swbc

Data Scientist II

San Antonio, TX

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 ...

SWBC

Data Scientist II

San Antonio, TX

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 ...

Staffingine LLC

Data Scientist

Redmond, WA · On-site

We are looking for a Data Scientist with strong experience in Python, SQL, and Machine Learning. The candidate should be comfortable working with large datasets, building predictive models, and ...

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Launch Consulting

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 ...

Compunnel

Data Scientist

San Francisco, CA · On-site

JOB SUMMARY The Data Scientist will serve as an AI/ML subject matter expert, focusing on consulting ... AWS AI/ML services (SageMaker, Bedrock preferred). - Experience working with our tech stack ...

United Airlines

AWS Data Scientist

Atlanta, GA

$80K - $90K/yr

Extensive experience with AWS, especially SageMaker, S3, Redshift, Glue, Athena, Lambda * Strong ... Design, develop, and deploy AI/ML models and data science solutions on AWS * Build end-to-end ML ...

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All JobsData Scientist With Sagemaker Jobs

Data Scientist With Sagemaker information

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$37.5K

$122.7K

$196.5K

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

As of Jul 5, 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 the highest paid job in data science?

The highest paid roles in data science typically include senior data scientists, machine learning engineers, and data science managers, especially those with expertise in advanced tools like SageMaker, deep learning, and big data platforms. These positions often require extensive experience, specialized skills, and sometimes certifications, leading to salaries that can exceed $150,000 annually depending on the industry and location.

Can data scientists make $300k?

Data scientists with experience, advanced skills, and expertise in tools like SageMaker can potentially earn $300,000 or more, especially in high-demand industries or senior roles. Achieving this salary often requires several years of experience, specialized knowledge, and sometimes leadership responsibilities.

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.

How does a Data Scientist working 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.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist, and many professionals successfully transition into the field at age 40 or later. Skills in programming, statistics, and tools like SageMaker can be acquired through online courses and certifications, making career changes feasible regardless of age.

Will AI replace data scientists?

Data scientists with expertise in tools like SageMaker play a key role in developing and deploying AI models. While AI can automate certain tasks, data scientists are essential for designing, interpreting, and improving models, making their role complementary rather than replaceable by AI alone.

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.

What are the key skills and qualifications needed to thrive as a Data Scientist with SageMaker, and why are they important?

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.
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:
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What job categories do people searching Data Scientist With Sagemaker jobs look for? The top searched job categories for Data Scientist With Sagemaker jobs are:
Data Scientist With Sagemaker jobs near you
Infographic showing various Data Scientist With Sagemaker job openings in the United States as of June 2026, with employment types broken down into 44% Full Time, 55% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Data Scientist

Data Scientist

Neptune

Duluth, GA • On-site

Full-time

Posted 17 days ago

Job description

Position Summary
As a Data Scientist, you will be responsible for developing and implementing machine learning models and analytical solutions that support our water utility intelligence platform. This position involves analyzing large-scale IoT data from water meters, building predictive models, and collaborating with cross-functional teams to deploy data science solutions into production. You will work closely with senior data scientists, software engineers, and Product Management to translate business requirements into actionable insights and ML capabilities. This role offers the opportunity to grow your skills in production ML, cloud technologies, and contribute directly to water conservation and utility operational improvements.
Responsibilities
• Collaborate with team members to develop and deploy machine learning models and data science solutions.
• Work with Product Management to understand requirements and translate them into analytical approaches.
• Build machine learning models for water consumption forecasting, anomaly detection, leak detection, and predictive maintenance.
• Analyze large-scale time-series data from IoT devices and water utility operations.
• Develop data processing workflows using Python, SQL, and distributed computing frameworks.
• Conduct exploratory data analysis to identify patterns, trends, and insights in utility data.
• Perform feature engineering and model experimentation to improve predictive performance.
• Create data visualizations and reports to communicate findings to stakeholders.
• Implement data quality checks and validation procedures for analytical pipelines.
• Collaborate with software engineers to integrate ML models into Neptune 360 platform.
• Monitor model performance and contribute to maintenance of production ML systems.
• Document analytical methodologies, code, and model implementations.
• Participate in code reviews and follow data science best practices.
• Work with cloud-based data infrastructure and ML tools (AWS preferred).
• Stay current with developments in machine learning and data science techniques.
• Participate in sprint planning and demonstrate completed work at the end of every iteration.
• Support senior data scientists with complex analytical projects.
• Continuously develop technical skills through self-directed learning and training.
Experience
• 3+ years of experience in data science, machine learning, or related analytical roles.
• 3+ years of experience with Python and data science libraries (pandas, NumPy, scikit-learn).
• Strong experience with SQL and relational databases.
• Experience building and evaluating machine learning models.
• Understanding of statistical analysis and experimental design principles.
• Experience with data visualization tools and techniques.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Experience with version control systems (Git).
• Understanding of software development best practices.
• Ability to work in Agile/iterative development environments.
• Strong problem-solving skills and attention to detail.
• Ability to communicate technical concepts clearly to both technical and non-technical
audiences.
• Demonstrated ability to learn new technologies and tools quickly.
• Continued professional development through courses, certifications, or projects.
• Preferred: Experience with PySpark or distributed computing frameworks.
• Preferred: Experience with time-series analysis and forecasting.
• Preferred: Experience with AWS services (SageMaker, Lambda, S3, Redshift).
• Preferred: Experience with deep learning frameworks (TensorFlow, PyTorch).
• Preferred: Experience deploying models to production environments.
• Preferred: Experience with IoT data or utility operations.
Education
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or
related quantitative field, or combination of education and equivalent experience.
Location: Duluth, GA
#HP1
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For further information, please review the Know Your Rights notice from the Department of Labor.

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