Entry Level Data Scientist

Brown And Caldwell is hiring an Entry Level Data Scientist in Walnut Creek, CA. The role involves developing and implementing machine learning models for water and wastewater management, collaborating with engineers and data scientists, and enhancing operational efficiency. Candidates should have a relevant degree and experience in machine learning, programming skills in Python/R, and knowledge of data analysis techniques. Familiarity with geospatial data and water treatment processes is a plus.
About the position

As a Data Scientist at Brown and Caldwell, you will play a crucial role in advancing our capabilities by developing and implementing machine learning models, algorithms, and data-driven solutions tailored to the field of water and wastewater management. Your work will directly impact our ability to optimize resource utilization, predict maintenance needs, and enhance overall operational efficiency for public and private water and wastewater utilities.

Responsibilities
• Collaborate with cross-functional teams of environmental engineers, data scientists, and software developers to identify and define machine learning opportunities in the water and wastewater industry.
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• Develop and deploy machine learning models to extract insights from various data sources, such as sensor data, historical records, and geospatial information.
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• Collaborate with data engineers to identify requirements for efficient storage and retrieval of relevant data, ensuring data quality and integrity.
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• Apply data preprocessing, exploratory data analysis, feature engineering, and model selection techniques to enhance the accuracy and performance of predictive models.
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• Develop algorithms for predictive maintenance, anomaly detection, optimization of industry processes, and condition-based asset management.
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• Monitor and apply industry trends and research advancements in machine learning, environmental engineering, and wastewater treatment to drive continuous innovation.
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• Represent BC at conferences and in publications through technical presentations and papers.

Requirements
• Bachelor's, Master's, or Ph.D. degree in Civil/Environmental Engineering, Environmental Science, or a related field with a focus on water resources, water treatment, and/or wastewater treatment.
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• Bachelor's, Master's or Ph.D. degree in Computer Science, Data Science, Engineering, or a related field with a focus on machine learning, artificial intelligence, or data science coursework and/or research.
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• Proven experience (2+ years) in developing and deploying machine learning models, algorithms, and data-driven solutions.
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• Advanced knowledge of statistics, data preprocessing, exploratory data analysis, feature engineering, and model selection techniques to enhance the accuracy and performance of predictive models.
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• Strong programming skills in languages such as Python and R, along with proficiency in relevant libraries and frameworks (e.g., TensorFlow, MLFlow, PyTorch, scikit-learn, DARTS, RLLib, etc).
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• Demonstrated knowledge of software engineering principles, version control, and best practices for writing clean, maintainable, and scalable code including development of custom GitHub Actions and Workflows for CI/CD.
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• Experience working with large datasets, databases, and data integration from various sources.

Nice-to-haves
• Experience optimizing and tuning ML algorithms with standard search methods and advanced automatic ML tuning methods.
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• Experience detecting and mitigating bias and unfairness in ML algorithms.
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• Experience using Gen AI infrastructure, LLM, and/or applying generative models to solve problems.
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• Familiarity with geospatial data processing and analysis tools is a plus.
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• Knowledge of water/wastewater treatment processes, linear asset management principles, and environmental engineering concepts is desirable.
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• Strong problem-solving abilities and the ability to translate business requirements into technical solutions.
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• Excellent communication skills to effectively collaborate with cross-functional teams and present findings to both technical and non-technical stakeholders.
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• A strong sense of innovation and the ability to stay updated with the latest advancements in machine learning and environmental engineering.

Benefits
• life insurance
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• parental leave
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• paid time off
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• paid holidays
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• tuition reimbursement
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• 401(k)