Data Scientist - environmental engineering

Brown and Caldwell•,

12d•$95,000 - $155,000•Onsite

About The Position

As a Data Scientist at Brown and Caldwell (BC), a leading environmental engineering firm, you will play a pivotal role in designing, building, and deploying advanced analytics and machine learning models that solve complex water and environmental challenges. We are using modern technology to transform the way that water is managed across the country. This Data Science role assists cross-functional teams with identifying and defining opportunities to use AI and ML in the water and wastewater industry. The role develops and deploys models, generative and agentic AI solutions, web applications, and geospatial analyses to extract insights from diverse data sources under the supervision and guidance of more experienced data scientists. The role also supports cloud-based application development and deployment for internal and external users in collaboration with multidisciplinary project teams. This role represents BC at conferences and in publications through technical presentations and papers. Work is performed under general supervision with limited autonomy. This role is strategically important because it sits at the intersection of BC’s engineering expertise and the rapidly expanding digital future of the water industry. It brings advanced analytics, AI, machine learning, and real-time decision support into a field where these capabilities are becoming essential for our clients. Your work will directly impact our ability to build digital solutions that ensure a future of clean water, thriving communities, and environmental protection. In-person interviews may be required.

Requirements

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.
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.
A degree in computer science, engineering, or related field or equivalent experience is required.

Nice To Haves

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.

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.