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UptimeAI

AI-driven predictive maintenance for industrial operations

Senior Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • A bachelor's degree or higher in Chemical Engineering, Mechanical Engineering, Aerospace Engineering, or Applied Physics with a heavy emphasis on computational modeling; candidates with Computer Science degrees are welcome if they have significant experience in heavy industry.
  • At least 5 years of hands-on experience developing and deploying machine-learning models within Manufacturing, Energy, Oil & Gas, or Power sectors.
  • Strong background handling time-series and sensor data from industrial or Internet of Things systems.
  • Hands-on experience with Python, TensorFlow or PyTorch, Scikit-learn, SQL, and cloud machine-learning platforms such as AWS, Azure, or GCP.
  • Excellent problem-solving and communication skills, with the ability to influence senior stakeholders.
  • Demonstrated ability to lead and mentor teams while contributing hands-on.
  • Ability to work independently, with strong problem-solving and decision-making abilities.

Responsibilities

  • Design, develop, and deploy advanced artificial intelligence and machine-learning models for predictive maintenance, process optimization, and industrial performance intelligence.
  • Collaborate with Product, Engineering, and Customer Success teams to translate industrial-domain business problems into data science solutions.
  • Guide the team in handling complex, high-dimensional, and time-series sensor data from Internet of Things, Supervisory Control and Data Acquisition, and Distributed Control System platforms.
  • Drive model interpretability, scalability, and accuracy, ensuring robust performance in real-world production environments.
  • Integrate first-principles models, including mass and energy balances and kinetics, with data-driven machine learning to ensure physical consistency in model outputs.
  • Lead feature-engineering strategy for high-frequency telemetry data from Supervisory Control and Data Acquisition and Distributed Control System platforms, focusing on transient-state detection and steady-state identification.
  • Deploy robust, low-latency inference pipelines that meet industrial edge-computing constraints.
  • Partner with industry experts and customers to validate models, derive insights, and deliver measurable business outcomes.
  • Monitor emerging trends in artificial intelligence, machine learning, deep learning, and industrial AI and incorporate innovative techniques into the product roadmap.
  • Contribute thought leadership by publishing whitepapers and patents or presenting at industry conferences.
  • Mentor data scientists and provide technical leadership, supporting continuous upskilling and adoption of best practices.

About the Company

UptimeAI

AI-driven predictive maintenance for industrial operations

UptimeAI provides AI-driven predictive maintenance and operational efficiency solutions for industrial users. Its platform uses deep learning to learn how different parts of an operation interact, which lowers alarm fatigue and delivers precise alerts that identify the root cause of issues. This helps clients in sectors like chemicals, utilities, oil, metals, and aerospace reduce unscheduled maintenance and improve reliability. The product is delivered via a subscription model, giving ongoing access to software and support. UptimeAI differentiates itself through its system-level approach to relate components within an operation, focus on reducing maintenance costs and disruptions, and a team of industry experts and data scientists. The company aims to help customers save time and money by improving reliability and efficiency while maintaining margins.

Company size
51-200
Company stage
Series A
Total funding
$19M
Headquarters
South San Francisco, CA
Founded
2019

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