Data Scientist

SOLTECH; Duluth (GA); On-site; USD 120,000 - 160,000; Full time

Job summary

SOLTECH is seeking a Senior Data Scientist (Machine Learning & MLOps) to design, deploy, and operationalize production ML solutions for a water utility platform. You'll focus on scalable data pipelines, model deployment, monitoring, and governance to enable future AI initiatives across the business.

The role combines deep data science with ML engineering, working with streaming IoT data, PySpark, SQL, and AWS services to deliver real-time insights and reliable production systems.

Qualifications

• Strong background in data science with production ML experience.
• Proficient in Python and modern ML libraries; experience with streaming data helps.
• Hands-on MLOps experience: deployment, monitoring, versioning, and governance.

Responsibilities

• Design, deploy, and operationalize ML solutions at scale on AWS.
• Build scalable ML pipelines for training, validation, and deployment.
• Develop anomaly detection and predictive analytics for IoT data.
• Collaborate with software engineers to productionize models.
• Establish reusable ML frameworks and automate workflows.

Skills

Python

AWS SageMaker

MLOps

SQL

PySpark

Communication

Education

Bachelor's in CS/Stats/Math

Tools

Git

Docker

Apache Spark

Job description

Senior Data Scientist (Machine Learning & MLOps)

Our client is seeking a Data Scientist (Machine Learning & MLOps) to help build the next generation of its intelligent water utility platform. This is a highly hands-on role focused on designing, deploying, and operationalizing production machine learning solutions that process billions of IoT sensor readings each day.

You'll play a key role in establishing the organization's reusable machine learning framework, building scalable data pipelines, deploying models into production, and enabling future AI initiatives across the business. The ideal candidate combines deep data science expertise with strong machine learning engineering and MLOps experience, taking models from concept through production while building repeatable, automated workflows.

This is an opportunity to solve complex engineering and machine learning challenges while making a meaningful impact on water conservation, infrastructure management, and sustainability.

Key Responsibilities

• Design, build, deploy, and operationalize production-grade machine learning solutions using AWS services.
• Develop scalable, repeatable machine learning pipelines supporting model training, validation, deployment, monitoring, and lifecycle management.
• Build anomaly detection and predictive analytics models capable of supporting near real-time decision making.
• Engineer robust, production-scale data pipelines using AWS Glue, PySpark, SQL, and cloud-native technologies.
• Process and analyze large-scale streaming IoT data.
• Perform feature engineering, model experimentation, evaluation, and performance optimization for production environments.
• Deploy machine learning models using AWS SageMaker and implement monitoring, retraining, automation, and governance throughout the ML lifecycle.
• Collaborate with Product Management and software engineering teams to translate business challenges into scalable machine learning solutions.
• Design solutions that emphasize automation, repeatability, reliability, and operational excellence.
• Participate in architecture discussions, code reviews, and Agile development activities.
• Evaluate emerging machine learning technologies and AWS capabilities to continuously improve platform performance and scalability.

Required Experience & Qualifications

• 5+ years of experience designing and delivering production machine learning or advanced analytics solutions.
• Demonstrated success deploying machine learning models into production environments.
• Strong experience building scalable machine learning pipelines and production data workflows.
• Hands-on experience with AWS SageMaker, AWS Glue, and related AWS analytics services.
• Strong production experience with PySpark and distributed data processing.
• Experience building or supporting MLOps practices, including model deployment, monitoring, automation, versioning, and lifecycle management.
• Experience processing large-scale datasets using distributed computing technologies.
• Experience supporting streaming or near real-time data processing environments.
• Strong Python programming skills utilizing modern machine learning libraries.
• Advanced SQL proficiency.
• Strong understanding of feature engineering, model evaluation, experimentation, and production optimization.
• Experience collaborating closely with software engineers to integrate machine learning solutions into production applications.
• Excellent analytical, problem-solving, and communication skills with the ability to translate business problems into scalable technical solutions.

Preferred Qualifications

• Experience with ClickHouse or other high-performance analytical databases.
• Experience building production solutions using streaming data technologies.
• Experience with anomaly detection, predictive maintenance, forecasting, or other advanced machine learning techniques.
• Experience working with large-scale IoT or time-series datasets.
• Background in utilities, industrial IoT, manufacturing, or other data-intensive operational environments.

What Will Make You Successful

We're looking for someone who enjoys solving complex engineering challenges—not simply building models in notebooks. The ideal candidate has experience taking machine learning solutions from concept through production, understands how to operationalize models at scale, and enjoys building reusable frameworks that enable future AI initiatives.

Success in this role requires an engineering mindset, strong business curiosity, and the ability to build scalable, production-ready machine learning solutions that deliver measurable business value. Candidates whose experience is primarily centered on reporting, dashboards, or ad hoc analytics will likely not be the best fit.

Education

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or another quantitative discipline, or an equivalent combination of education and practical experience.