Senior Data Scientist, Fleet Health

Description

The Data Scientist, Fleet Health will be a key member of Gridware’s Fleet team, focused on building fleet health predictive models, thresholds, and optimization frameworks to ensure 99% uptime across our rapidly scaling network of IoT devices. This role will drive the development of advanced intelligence for forecasting device health, predicting potential installation performance, and establishing data-driven criteria for detecting and remediating device degradation.

• Communicate insights and contribute to scalable fleet intelligence frameworks that support growth from tens of thousands to millions of devices.
• Design and evaluate statistical tests and simulations to measure impact, uncover downtime drivers, and identify optimization opportunities.
• Define, test, and refine health thresholds to classify degradations, detect regressions, and optimize fleet performance across diverse operating conditions.
• Collaborate with engineering, fleet operations, and software teams to embed intelligence into automated monitoring and remediation pipelines at scale.
• Develop predictive and forecasting models to anticipate device health issues, solar availability, and connectivity performance, driving proactive interventions and deployment planning.

Requirements

The ideal candidate will bring a strong foundation in applied machine learning and forecasting, comfort working with real-world sensor and fleet data, and a passion for scaling intelligent systems that power resilient clean energy infrastructure. You will work closely with engineering, operations, and product teams to apply insights that both optimize today’s fleet performance and inform the evolution of tomorrow’s solutions.

• Exposure to distributed sensing systems in energy, seismic monitoring, aerospace, industrial IoT, or environmental science.
• Background in working with power-constrained, communication-constrained, or solar-powered devices.
• 5+ years of applied experience in data science with large-scale, real-world systems (IoT, clean tech, connectivity, or related domains).
• Expertise in forecasting, anomaly detection, and predictive modeling, with experience handling time-series, sensor, or geospatial datasets.
• Strong communicator with the ability to design experiments, validate models, and translate technical findings into actionable insights for cross-functional teams.
• Experience forecasting solar generation, connectivity performance, or energy resource availability.
• Proficiency in Python with scientific and ML libraries (NumPy, Pandas, SciPy, scikit-learn, Keras, PyTorch) and familiarity with data platforms (SQL, Spark, etc.).
• Experience scaling predictive systems across large and growing hardware fleets.
• Bachelor’s, Master’s degree, or PhD in Engineering, Statistics, Data Science, or a related quantitative field.

Benefits

Paid parental leave

Company-paid training

Commuter allowance

Alternating day off (every other Monday)

Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)

“Off the Grid”, a two week per year paid break for all employees.

Training + Development

Interview process

Visa Sponsorship

Security clearance