Data Scientist – Environmental Engineering

Brown and Caldwell

. Design, build, and deploy advanced analytics and machine learning models for water and environmental challenges .

Posted 9/25/2026full-timeRemote • United StatesJuniorMid-Level💰 $95,000 - $155,000 per yearWebsite

Tech Stack

Tools & technologies

AzureCloudIoTNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, build, and deploy advanced analytics and machine learning models for water and environmental challenges
Identify and define AI and ML opportunities in the water and wastewater industry
Develop and deploy models, generative and agentic AI solutions, web applications, and geospatial analyses
Support cloud-based application development and deployment for internal and external users
Conduct exploratory data analysis, model building, statistical analysis, feature extraction, and hyperparameter tuning
Set up data storage systems, preprocess data, and create data visualizations
Build and optimize machine learning models
Follow DevOps, software engineering, version control, and model deployment best practices
Collaborate with cross-functional and multidisciplinary project teams to understand data needs
Present findings to internal stakeholders and support client-facing presentations
Monitor industry trends and research advancements in machine learning and water-sector applications
Represent Brown and Caldwell at conferences and in technical publications
Provide mentorship and knowledge-sharing to less experienced team members as needed
Execute additional assignments based on evolving needs

Requirements

What you’ll need

Minimum 2 years of Data Science or related experience
Typically certified in the SMS Framework and progressing through SMS competencies
Strong programming skills in Python and R
Proficiency in relevant libraries and frameworks
Clean, maintainable, scalable code development with minimal oversight
Basic knowledge of water, wastewater, environmental engineering, and science topics
Degree in computer science, engineering, or related field, or equivalent experience
High proficiency in Python, pandas, NumPy, scikit-learn, and SQL
Experience developing and deploying generative AI applications, retrieval systems, and agentic workflows
Experience building data-driven applications, APIs, web tools, and interactive user interfaces
Experience with cloud-based solutions, DevOps, Azure, DevSecOps, and MLOps
Experience with Azure Machine Learning Studio, Azure Databricks, Azure Synapse Analytics, and Azure Cognitive Services/OpenAI API integration
Experience developing data-processing workflows for ML algorithms
Understanding of regression, clustering, random forests, gradient boosting, neural networks, and statistics
Experience with TensorFlow and PyTorch
Knowledge of software engineering principles, Git, and production-ready code
Experience with CI/CD for ML, such as GitHub Actions or Azure DevOps
Experience with streaming data processing and time-series forecasting for IoT and edge computing
Proficiency in cloud infrastructure and web application development
Experience with geospatial data processing, spatial analysis, and interactive mapping
Ability to engage clients, identify needs, and position data-driven consulting solutions
Strong problem-solving skills and ability to work collaboratively across functions
Excellent communication skills with technical and non-technical stakeholders
Willingness to stay current with data science and environmental engineering trends
Subject to pre-employment background check and drug test

Benefits

Comp & perks

Medical, dental, and vision insurance
Short- and long-term disability insurance
Life insurance
Employee assistance program
Paid time off
Parental leave
Paid holidays
401(k) retirement savings plan with employer match
Performance-based bonus eligibility
Employee referral bonuses
Tuition reimbursement
Pet insurance
Long-term care insurance
Exceptional development opportunities