SOLTECHOur 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. Key Responsibilities
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 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 Required Experience & Qualifications
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, NumPy, 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 Preferred Qualifications
Experience with PySpark or distributed computing frameworks
Experience with time-series analysis and forecasting techniques
Hands-on experience with AWS services such as SageMaker, Lambda, S3, or Redshift
Experience with deep learning frameworks (TensorFlow or PyTorch)
Experience deploying machine learning models into production environments
Background working with IoT data or within utility operations Education
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
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