Senior Data Scientist

Senior Data Scientist

About UptimeAI:

UptimeAI provides AI reasoning agents that help industrial operations teams make goal-oriented, real-time decisions.

Industrial facilities are rich in data but constrained by the expertise required to maintain and interpret it. As experienced engineers retire and operational complexity grows, organizations struggle to scale the expert judgment needed to maintain reliability, safety, and performance.

UptimeAI addresses this challenge by embedding engineering reasoning directly into AI agents that continuously analyze operational data, diagnose issues, and guide teams toward the best actions. The result is faster decisions, reduced downtime, improved efficiency, and stronger operational performance.

Role: Senior Data Scientist

Location: Bangalore- Karnataka (Hybrid)

Experience: 5+ years

Department: Data Science

Role Overview

We are looking for individuals who sit at the intersection of Data Science and Physical Sciences. You understand that industrial data isn't just rows in a database—it represents thermo dynamics, fluid dynamics, and mechanical stress. The candidate should be capable of translating business problems into scalable AI solutions, leveraging creative thinking and first-principles problem solving.

This role requires someone who can:

• Build and deploy production-grade AI systems, not just prototypes

• Design agentic workflows that can reason, adapt and take actions in dynamic environments

• Work with noisy, incomplete, and real-time industrial data to derive meaningful insights

• Collaborate closely with domain experts (e.g., operations, reliability, or plant engineers) to create context-aware AI solutions

• Demonstrate innovative thinking in solving ambiguous problems where standard approaches may not apply

Key Responsibilities

• Be a part of the design, development, and deployment of advanced AI/ML models for predictive maintenance, process optimization, and industrial performance intelligence

• Collaborate closely with Product, Engineering, and Customer Success teams to translate business problems from industrial domains into data science solutions.

• Guide the team in handling complex, high-dimensional, and time-series sensor data from IoT, SCADA, and DCS systems.

• Drive model interpretability, scalability, and accuracy, ensuring robust performance in real-world production environments.

• Hybrid Modeling: Integrate First-Principles Models (mass/energy balances, kinetics) with data-driven ML to ensure physical consistency in model outputs.

• Signal Processing: Lead the strategy for feature engineering on high-frequency telemetry data from SCADA/DCS, focusing on transient state detection and steady-state identification.

• Operationalization: Move beyond "notebook AI" to deploy robust, low-latency inference pipelines that respect the edge-computing constraints of industrial environments.

• Partner with industry experts and customers to validate models, derive insights, and deliver measurable business outcomes.

• Stay ahead of emerging trends in AI/ML, deep learning, and industrial AI to incorporate innovative techniques into UptimeAi’s product roadmap.

• Contribute to thought leadership by publishing whitepapers, patents, or presenting at industry conferences

• Mentor and provide technical leadership to data scientists, ensuring continuous upskilling and adoption of best practices

Qualifications

• Bachelor’s or higher in Chemical Engineering, Mechanical Engineering, Aerospace, or Applied Physics with a heavy emphasis on computational modeling. (Candidates with CS degrees are welcome if they have significant experience in heavy industry).

• 5+ years of hands-on experience developing and deploying ML models specifically within Manufacturing, Energy, Oil & Gas, or Power sectors.

• Strong background in handling time-series and sensor data from industrial or IoT systems.

• Hands-on experience with Python, TensorFlow/PyTorch, Scikit-learn, SQL, and cloud ML platforms (AWS, Azure, GCP).

• Excellent problem-solving and communication skills with the ability to influence senior stakeholders.

• Demonstrated ability to lead and mentor teams while being a hands-on contributor

• Ability to work independently, with strong problem-solving and decision-making abilities

Why to join UptimeAI:

• Impact Industry-Wide Change: Contribute to transformative solutions that significantly improve operational efficiency and reliability for global clients.

• Collaborative and Growth-Oriented Environment: Join a talented, passionate team that values innovation, continuous learning, and professional growth.

• Opportunities for Leadership and Innovation: Lead pioneering projects, influence product development, and shape the future of industrial AI solutions.

A Senior Data Scientist role focused on production AI/ML for industrial operations, combining data-driven methods with physical models to address reliability and performance challenges. You will work with sensor and process data, collaborate with industrial experts, and guide model development through deployment in operational environments.

Role at a glance

Skills mentioned

What you will likely work on

• Design, develop, and deploy AI/ML models for predictive maintenance, process optimization, and industrial performance.
• Build reasoning workflows for dynamic operational settings and production-ready inference systems.
• Analyze high-dimensional, noisy, real-time time-series data from industrial sensors and IoT, SCADA, and DCS systems.
• Combine first-principles physical models with data-driven ML and develop signal features for transient and steady-state detection.
• Improve model interpretability, accuracy, scalability, and reliability in production environments, including low-latency edge deployments.
• Partner with product, engineering, customer success, industrial experts, and customers to frame problems, validate models, and deliver measurable outcomes.
• Mentor data scientists and provide technical direction, while contributing hands-on.
• Track emerging AI/ML methods and contribute to product direction and technical thought leadership through publications, patents, or conference presentations.

Good fit signals

• At least five years developing and deploying ML models in manufacturing, energy, oil and gas, or power.
• Experience handling industrial or IoT time-series and sensor data, ideally in production contexts.
• A computational modeling background in chemical, mechanical, or aerospace engineering or applied physics; computer science candidates are also considered with significant heavy-industry experience.
• Able to connect physical-science reasoning with data-driven models for industrial systems.
• Comfortable mentoring colleagues and influencing senior stakeholders while remaining a hands-on contributor.