Environmental Data Scientist Jobs in Georgia (NOW HIRING)

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.

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