About the job
About SewerAI Corporation
SewerAI is revolutionizing the management of underground infrastructure through our innovative AI-driven inspection and risk assessment platform. We empower contractors, engineering firms, and utility companies to derive actionable insights from sewer inspection data, dramatically reducing the time spent on manual video analysis from hours to mere minutes. Following a remarkable doubling of our customer base in the last year, we are poised for an exciting growth trajectory.
About the Role
We are seeking a Senior Data Scientist to join our AI team, specializing in applied machine learning for sewer infrastructure. In this pivotal role, you will develop advanced anomaly detection systems, time series forecasting models, and predictive analytics to assist utilities in anticipating and mitigating infrastructure failures.
Collaboration with our engineering and data teams will be essential as you construct machine learning systems that analyze sensor data, detect anomalies, and provide actionable insights to our clientele.
What You'll Work On
Anomaly Detection — Create models to spot unusual patterns and potential problems in sewer infrastructure data.
Time Series Forecasting — Develop predictive models for maintenance scheduling and assessing infrastructure health.
ML Model Development — Design, train, and refine machine learning models tailored for sewer-specific applications.
Data Analysis & Pipelines — Construct pipelines for large-scale processing and analysis of infrastructure data.
Dashboards & Reporting — Generate visualizations and reports in Hex to present insights to stakeholders.
Required Technical Skills
Python — Proficient experience with pandas, scikit-learn, and statistical analysis techniques.
Deep Learning Frameworks — Experience with PyTorch or TensorFlow for complex modeling tasks.
SQL — Capable of executing complex queries, including window functions and analytical queries.
Time Series Analysis — Expertise in forecasting techniques, trend analysis, handling seasonality, and signal processing.
Anomaly Detection — Familiarity with statistical and machine learning-based anomaly detection methodologies.
Data Stack — Familiarity with tools like Hex, dbt, and ClickHouse.