Senior Data Scientist

📋 Description

• • Design, develop, and deploy production-grade AI/ML models for predictive maintenance, process optimization, and industrial performance intelligence using real-time sensor data from SCADA, DCS, and IoT systems.
• • Build agentic workflows capable of reasoning, adapting, and taking actions in dynamic industrial environments with high uncertainty and noisy data.
• • Integrate first-principles physical models (mass/energy balances, thermodynamics, fluid dynamics, kinetics) with data-driven machine learning to ensure physically consistent and interpretable outputs.
• • Lead feature engineering strategies for high-frequency telemetry data, focusing on transient state detection, steady-state identification, and signal denoising techniques.
• • Operationalize machine learning models into low-latency, edge-compliant inference pipelines that meet the constraints of industrial environments and real-time decision-making requirements.
• • Collaborate directly with domain experts—operations, reliability, and plant engineers—to translate physical process challenges into data science problems and validate model outputs against real-world operational conditions.
• • Drive model interpretability, scalability, and robustness in production to ensure trust and adoption by non-technical industrial stakeholders.
• • Guide teams in handling complex, high-dimensional time-series datasets from heavy industrial equipment, applying advanced signal processing and anomaly detection techniques.
• • Stay current with emerging trends in industrial AI, deep learning, and edge AI, and incorporate innovative techniques into UptimeAI’s product roadmap.
• • Contribute to thought leadership by authoring whitepapers, filing patents, or presenting at industry conferences on AI applications in manufacturing, energy, or power sectors.
• • Mentor junior data scientists and technical staff, fostering best practices in model development, version control, MLOps, and cross-functional collaboration.
• • Work hybridly from Bangalore to align closely with Product, Engineering, and Customer Success teams to ensure solutions are customer-focused and business-impact-driven.
• • Translate ambiguous, high-stakes industrial problems into scalable AI solutions using first-principles reasoning and creative problem-solving, where standard approaches often fail.
• • Ensure all AI solutions deliver measurable business outcomes such as reduced downtime, improved asset utilization, and enhanced operational reliability for enterprise clients.
• • Maintain deep technical involvement while providing leadership, balancing hands-on modeling work with strategic direction for the data science team.
• • Work across the full lifecycle of AI systems—from problem framing and data acquisition to deployment, monitoring, and iterative improvement in live industrial settings.