Data Scientist - R01555477

📋 Description

• • Join Brillio as a Data Scientist and become the analytical engine behind the next generation of intelligent electrical-grid solutions. You will own the full data-science lifecycle—from wrangling petabyte-scale SCADA, AMI, and CIM network-model feeds to deploying real-time ML models that predict DER impacts, optimize load flows, and harden grid resilience for millions of end-users.
• • Architect and curate massive, unstructured data sets that capture every heartbeat of the grid: three-phase power-flow snapshots, protection-relay events, distribution-automation logs, and behind-the-meter solar and storage telemetry. You will design scalable, cloud-native data stores and define ingestion patterns that guarantee sub-second latency for operational dashboards and long-term archival for regulatory analytics.
• • Lead exploratory data analyses that uncover hidden patterns in voltage sags, fault currents, and dynamic load signatures. Translate these insights into predictive models that anticipate equipment failures, quantify renewable-integration risk, and recommend optimal capacitor-bank or recloser settings—turning raw sensor noise into actionable intelligence for control-room operators.
• • Partner elbow-to-elbow with Data Engineers to review and optimize ETL/ELT pipelines, ensuring that every kilobyte of SCADA and AMI data is cleansed, enriched, and version-controlled. You will specify schema evolutions, champion data-quality SLAs, and institute automated testing harnesses that catch anomalies before they corrupt downstream models.
• • Own the design and delivery of advanced analytics products—think probabilistic load-flow engines built in Python that fuse OpenDSS, GridLAB-D, and Matpower simulations with live AMI streams. You will craft containerized micro-services that scale horizontally on Kubernetes and expose REST/GraphQL endpoints for grid-planning and operations teams.
• • Direct the creation of sophisticated machine-learning systems: gradient-boosted models that forecast distributed PV ramp rates, graph neural networks that learn topology reconfiguration strategies, and reinforcement-learning agents that autonomously balance volt/VAR in the presence of high DER penetration. Validate algorithmic efficacy through rigorous back-testing against historical storm events and N-1 contingency scenarios.
• • Serve as the data-science thought leader within cross-functional agile squads. Collaborate with scrum masters, product owners, and electrical-grid engineers to translate complex regulatory and business requirements into sprint-ready user stories. Champion MLOps best practices—CI/CD for notebooks, feature stores, model registries, and automated retraining pipelines.
• • Establish the strategic roadmap for data science at Brillio, evangelizing cutting-edge research in probabilistic power-system analysis, federated learning across utility boundaries, and privacy-preserving analytics for customer-meter data. Influence C-suite stakeholders on capital-allocation decisions by quantifying the ROI of grid-modernization initiatives through scenario modeling.
• • Design and execute comprehensive testing strategies for algorithmic robustness: Monte-Carlo simulations that stress-test models against extreme weather, synthetic data generators that augment rare fault events, and fairness audits that ensure equitable voltage regulation across diverse socio-economic communities.
• • Build and mentor a high-performing data-science pod, providing hands-on coaching in Python, PySpark, TensorFlow, and domain-specific toolkits like PyPSA and Pandapower. Foster a culture of reproducible research, peer code review, and continuous learning through internal tech talks and external conference participation.
• • Oversee the transition of prototypes to production-grade solutions, partnering with DevOps to implement blue-green deployments, real-time model monitoring, and automated rollback triggers. Ensure 99.9 % uptime for mission-critical analytics services that operators rely on during peak-load and emergency-restoration events.
• • Continuously monitor model drift and business KPIs—SAIDI/SAIFI improvements, peak-demand reduction, renewable curtailment avoidance—and iterate rapidly to sustain measurable value. Present findings to utility executives, regulators, and industry forums, cementing Brillio’s reputation as the premier innovator in data-driven grid optimization.