Data Scientist - environmental engineering

Brown and Caldwell is a leading environmental engineering firm focused on transforming water management through modern technology. The Data Scientist designs, builds, and deploys analytics, machine learning, AI, web application, and geospatial solutions for water and wastewater challenges. The role collaborates with multidisciplinary teams, presents findings, supports cloud-based deployments, and contributes to technical publications and conferences under general supervision.

Responsibilities:

• Conduct complex data analyses
• Execute data science tasks such as exploratory data analysis, model building, statistical analysis, and feature extraction, and hyperparameter tuning
• Set up data storage systems, preprocess data and create compelling data visualizations in standard platforms
• Build and optimize machine learning models
• Follow DevOps best practices for model deployment
• Collaborate with cross-functional teams to understand data needs
• Present findings to internal stakeholders and support client-facing presentations
• Follow best practices for software engineering and version control
• Monitor and apply industry trends and research advancements in machine learning and applications in the water sector
• Flexibility to adapt and execute various additional assignments based on evolving needs
• May provide mentorship, guidance, support, and knowledge-sharing to help less experienced team members develop their skills and grow within their roles

Requirements:

• • Strong programming skills in languages such as Python and R, along with proficiency in relevant libraries and frameworks
• • Proficiency in writing clean, maintainable, and scalable code with minimal oversight
• • Strong communication skills and ability to present findings with some review and oversight
• • Basic knowledge of water/wastewater/environmental engineering and science topics
• • Typically, a minimum of 2 years of Data Science or related experience is required
• • Typically certified in the SMS Framework, and progressing through the SMS competencies
• • A degree in computer science, engineering, or related field or equivalent experience is required
• In-person interviews may be required
• • 4+ years of related work experience
• • Core Programming: 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 developing cloud-based solutions and follow DevOps best practices for model and application deployment
• • Experience with Azure platform, DevSecOps, and MLOps, specifically:
• • Azure Machine Learning Studio (managing experiments, model registry, endpoints)
• • Azure Databricks and Azure Synapse Analytics
• • Azure Cognitive Services / OpenAI API integration
• • Experience developing functional data processing workflows to transform raw data into reliable input for ML algorithms
• • Machine Learning & Statistics: Solid understanding of ML algorithms (Regression, Clustering, Random Forests, Gradient Boosting, Neural Networks) and statistical concepts. Experience with Deep Learning frameworks (TensorFlow, PyTorch)
• • MLOps & Engineering: Demonstrated knowledge of software engineering principles, version control (Git), and best practices for writing clean, production-ready code. Experience with CI/CD for ML (e.g., GitHub Actions, Azure DevOps)
• • IoT & Time-Series: Experience with streaming data processing and time-series forecasting, particularly for IoT and edge compute applications
• • Experience with large language models, retrieval, and agentic workflow development
• • Proficiency in cloud infrastructure and web application development
• • Experience with geospatial data processing, spatial analysis, and interactive mapping
• • Demonstrated ability to engage with clients to identify needs and effectively sell or position data–driven consulting solutions, including translating technical capabilities into clear business value
• • Strong problem-solving skills and the ability to work in a collaborative, cross-functional environment
• • Excellent communication skills to interact with technical and non-technical stakeholders, with the ability to explain complex model outputs to engineering leaders
• • A passion for staying updated with the latest trends, tools, and technologies in data science and environmental engineering
• • Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related field