Data Scientist

Our client is seeking a Data Scientist to help power an innovative water utility intelligence platform. In this role, you will develop and deploy machine learning models and advanced analytics solutions that transform large-scale IoT water meter data into actionable insights. You’ll collaborate with cross‑functional teams to bring predictive models into production and directly contribute to water conservation and operational efficiency initiatives.

This is an exciting opportunity to grow your expertise in production‑grade machine learning, cloud technologies, and data engineering while making a meaningful impact in the utilities and sustainability space.

• Design, develop, and deploy machine learning models and scalable data science solutions
• Partner with Product Management to translate business requirements into analytical strategies and ML capabilities
• Build predictive models for water consumption forecasting, anomaly detection, leak detection, and predictive maintenance
• Analyze large-scale, time‑series IoT data from water meters and utility operations
• Develop and optimize data pipelines using Python, SQL, and distributed computing frameworks
• Perform exploratory data analysis (EDA) to uncover trends, patterns, and performance insights
• Conduct feature engineering, model experimentation, and performance tuning
• Create clear data visualizations and reports to communicate insights to technical and non‑technical stakeholders
• Implement data validation, quality assurance checks, and monitoring processes within analytical workflows
• Collaborate with software engineers to integrate machine learning models into the client’s Neptune 360 platform
• Monitor model performance and support ongoing maintenance of production ML systems
• Document methodologies, code, and model development processes
• Participate in code reviews and uphold data science and software engineering best practices
• Work within cloud‑based data infrastructure environments (AWS preferred)
• Stay current with emerging machine learning techniques, tools, and industry trends
• Participate in Agile sprint planning and present completed work at the end of each iteration
• Support senior data scientists on complex analytical initiatives
• Continuously expand technical skills through training, certifications, and hands‑on learning

• 3+ years of experience in data science, machine learning, or a related analytical field
• 3+ years of hands‑on experience with Python and data science libraries (pandas, Num Py, scikit‑learn)
• Strong proficiency in SQL and relational databases
• Proven experience building, evaluating, and validating machine learning models
• Solid understanding of statistical analysis and experimental design
• Experience with data visualization tools and best practices
• Familiarity with cloud platforms (AWS, Azure, or GCP)
• Experience using version control systems such as Git
• Understanding of software development lifecycle and best practices
• Experience working in Agile or iterative development environments
• Strong analytical thinking, problem‑solving skills, and attention to detail
• Ability to communicate complex technical concepts to both technical and non‑technical audiences
• Demonstrated ability to learn new technologies quickly and adapt in a fast‑paced environment
• Ongoing professional development through coursework, certifications, or applied projects

• Experience with PySpark or distributed computing frameworks
• Experience with time‑series analysis and forecasting techniques
• Hands‑on experience with AWS services such as Sage Maker, Lambda, S3, or Redshift
• Experience with deep learning frameworks (Tensor Flow or PyTorch)
• Experience deploying machine learning models into production environments
• Background working with IoT data or within utility operations

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent combination of education and experience