Data Scientist
- Design and develop machine learning models tailored to water and wastewater operations, including (but not limited to):
- Chemical forecasting and dosing optimization
- Predictive maintenance for critical assets
- Process control optimization
- Analyze operational and sensor data (SCADA, AMI, lab results, etc.) to uncover actionable insights.
- Translate domain knowledge and operational challenges into structured ML problems.
- Work closely with subject matter experts (e.g., engineers, operators) to validate hypotheses and outputs.
- Collaborate with software engineers and product teams to productionize models and monitor their performance in the field.
- Document model architecture, assumptions, and limitations clearly for both technical and non-technical stakeholders.
- Continuously evaluate and improve model performance in real-world conditions.
Qualifications
- 4+ years of experience in machine learning, data science (bonus: in a SaaS or industrial/utility context)
- Master's degree in data science is preferred.
- Proven experience deploying models into production environments—not just research or prototyping.
- Strong programming skills in Python (e.g., scikit-learn, pandas, TensorFlow/PyTorch, etc.).
- Experience working with time-series data, anomaly detection, and predictive modeling.
- Familiarity with common water/wastewater datasets and operational processes is a major plus.
- Experience with edge or cloud-based model deployment (e.g., Azure, AWS, GCP), containers, and CI/CD workflow is preferred.
- Excellent communication skills—able to bridge technical and operational teams.
- Bonus points for experience in:
- SCADA / telemetry systems
- Digital twins or simulation modeling
- Optimization algorithms (e.g., genetic algorithms, reinforcement learning)
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