Data Scientist - environmental engineering; remote allowed

Data Scientist / Machine Learning Engineer 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. Your work will directly impact our ability to build digital solutions that ensure a future of clean water, thriving communities, and environmental protection.

BC’s Data Scientist 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.

• Modernizes BC's service offerings.
• Supports our engineers across all practices.
• Improves quality and speed of deliverables.
• Helps build a forward‑looking technical culture.
• Enhances our ability to generate and deliver digital value to clients.

• Data science + ML depth.
• Background in water, civil, or environmental engineering (or interest/willingness to learn the industry).
• Fresh ideas on how to refine/rethink methods on how BC delivers analytics.
• Aptitude to "sell" digital/AI solutions and communicate value to clients.

Remote work is allowed within the U.S.

• Collaborate with interdisciplinary teams of environmental engineers, data engineers, and software developers to translate complex environmental problems into scalable data science and machine learning solutions.
• Design, train, and validate machine learning models (supervised, unsupervised, and deep learning) using data from various sources, including high‑frequency IoT sensor data, historical records, and geospatial information.
• Lead the end‑to‑end ML lifecycle, from exploratory data analysis (EDA) and feature engineering to model training, hyperparameter tuning, and deployment.
• Develop and implement MLOps practices to ensure models are scalable, reproducible, and monitored for performance drift in production environments.
• Apply advanced statistical methods and time‑series analysis to support demand forecasting, anomaly detection in infrastructure, and predictive maintenance.
• Collaborate with Data Engineers to guide the design of data infrastructure, ensuring that datasets are optimized for analytical modeling and machine learning workloads.
• Stay up to date with emerging technologies, tools, and best practices in AI, Large Language Models (LLMs), and environmental engineering to drive continuous improvement.

• Bachelor's in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related field.
• Minimum of 5 years of experience in data science, machine learning engineering, or advanced analytics in a professional setting.
• Core Programming:
  High proficiency in Python (pandas, numpy, scikit‑learn) and SQL.
• Big Data & Distributed Computing:
  Hands‑on experience with PySpark and distributed training frameworks.
• Experience with Azure platform, Dev Sec Ops , 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.
• 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.
• 8+ years of experience in data science, machine learning engineering, or advanced analytics in a professional…