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.

Requirements

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, 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.
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.
Preferred Qualifications: 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 setting.
Solid understanding of ML algorithms (Regression, Clustering, Random Forests, Gradient Boosting, Neural Networks) and statistical concepts.
Experience with Deep Learning frameworks (TensorFlow, PyTorch).
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).
Experience with streaming data processing and time-series forecasting, particularly for IoT and edge compute applications.
Knowledge of geospatial data processing and analysis (GIS, GeoPandas).

Benefits

medical
dental
vision
short and long-term disability
life insurance
an employee assistance program
paid time off and 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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

PythonSQLPySparkMLOpsMachine LearningDeep LearningStatistical MethodsTime-Series AnalysisData Processing WorkflowsML Algorithms

Soft skills

Problem-SolvingCollaborationCommunicationInterdisciplinary TeamworkContinuous Improvement

Certifications

Bachelor's DegreeMaster's Degree