{"id":1207178,"url":"https://alion.io/job/gridware-senior-data-scientist-fleet-health","title":"Senior Data Scientist, Fleet Health","company":{"id":173845,"name":"Gridware","domain":"gridware.io","url":"https://alion.io/company/gridware-2","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":93,"open_postings":14,"ghost_share":0,"stale_share":0.071,"repost_share":0,"time_to_fill_p50_days":137,"computed_at":"2026-10-19T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":175000,"max":205000,"currency":"USD","period":"year","gross":null,"usd_annual":205000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Keras","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2025-09-25T20:41:59Z","employer_posted_date":"2025-09-25","last_verified_at":"2026-10-19T05:41:54Z","board_verified":true,"closed_at":null,"days_open":388,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":388},"description":"Role Description\nThe 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.\nThe 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.\nResponsibilities\nDevelop predictive and forecasting models to anticipate device health issues, solar availability, and connectivity performance, driving proactive interventions and deployment planning.\nDefine, test, and refine health thresholds to classify degradations, detect regressions, and optimize fleet performance across diverse operating conditions.\nDesign and evaluate statistical tests and simulations to measure impact, uncover downtime drivers, and identify optimization opportunities.\nCollaborate with engineering, fleet operations, and software teams to embed intelligence into automated monitoring and remediation pipelines at scale.\nCommunicate insights and contribute to scalable fleet intelligence frameworks that support growth from tens of thousands to millions of devices.\nRequired Skills\nBachelor’s, Master’s degree, or PhD in Engineering, Statistics, Data Science, or a related quantitative field.\n5+ years of applied experience in data science with large-scale, real-world systems (IoT, clean tech, connectivity, or related domains).\nExpertise in forecasting, anomaly detection, and predictive modeling, with experience handling time-series, sensor, or geospatial datasets.\nProficiency in Python with scientific and ML libraries (NumPy, Pandas, SciPy, scikit-learn, Keras, PyTorch) and familiarity with data platforms (SQL, Spark, etc.).\nStrong communicator with the ability to design experiments, validate models, and translate technical findings into actionable insights for cross-functional teams.\nBonus Skills\nExperience forecasting solar generation, connectivity performance, or energy resource availability. \nExposure to distributed sensing systems in energy, seismic monitoring, aerospace, industrial IoT, or environmental science. \nBackground in working with power-constrained, communication-constrained, or solar-powered devices. \nExperience scaling predictive systems across large and growing hardware fleets.","description_format":"text","description_chars":2863,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Sensors & Transducers","Power Grid"],"lifecycle":[{"event":"open","at":"2026-09-25T00:49:08Z"}],"visa":[],"liveness":{"score":11,"band":"cold","label":"Long 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