Data Scientist — Water Analytics & Production ML

Job Description

🔎 Key Responsibilities

• Develop and deploy machine learning models using large-scale IoT and sensor data.
• Build predictive systems for:
  • Water usage forecasting
  • Leak detection
  • Infrastructure optimization
  • Operational efficiency
• Work with Python and SQL to process and analyze datasets.
• Collaborate with cross-functional teams (engineering, product, domain experts).
• Translate business and environmental needs into scalable ML solutions.
• Support production ML pipelines and model monitoring.

🧠 Required Skills & Background

• 3+ years of experience in data science or applied ML.
• Strong programming in:
  • Python
  • SQL
• Experience with:
  • Machine learning modeling
  • Time-series or IoT data (preferred)
  • Data pipelines and production environments
• Degree in:
  • Data Science
  • Computer Science
  • Statistics
  • Engineering or related field

🌍 Why This Role Is Interesting

This position stands out because it combines:

• AI + Sustainability Impact → contributing to water conservation and infrastructure efficiency.
• IoT + Production ML → practical, real-world deployment instead of only research.
• Cross-disciplinary exposure → utilities, environmental systems, and analytics.

It’s especially valuable experience if you want careers in:

• Climate tech / sustainability tech
• Smart cities
• Industrial AI
• Applied machine learning engineering

✅ Ideal Candidate Profile

You’ll likely succeed if you:

• Enjoy working with messy real-world sensor data.
• Like deploying models into production (not just notebooks).
• Have curiosity about infrastructure or environmental systems.
• Can communicate technical insights to non-technical stakeholders.