Data Scientist

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: Data Scientist - Full-Stack Engineering

Location: Bangalore- Karnataka (Hybrid)

Experience: 3 - 5 years

Department: Data Science

Role Overview

We are looking for a unique breed of Data Scientist who thrives at the intersection of Agentic AI, Full-Stack Engineering, and Industrial Design Thinking. Sitting within the Data Science team, you will be the bridge between algorithmic research and tangible product impact. You aren't just building models in a vacuum; you are leveraging modern AI-native development tools (Claude Code, Cursor, Antigravity) to rapidly build integrated, production-grade prototypes that translate complex industrial data into intuitive, actionable AI experiences.

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

• Rapid DS Prototyping: Take early-stage ML models and RAG pipelines and transform them into interactive, full-stack "Alpha" products within days.

• Agentic UX Design: Architect front-end interfaces that allow users to interact with Agentic Workflows—designing how a plant engineer "talks" to a Root Cause Analysis (RCA) agent or probes a time-series plot.

• Data Translation: Deep-dive into customer datasets (SCADA, DCS, IoT) to perform exploratory data analysis (EDA) and immediately reflect those insights in a functional UI.

• Bridge to Engineering: Work with core Data Science to understand model constraints and with Engineering to ensure your high-fidelity prototypes are architected for scale (API-first, containerized).

• Design-Led Data Science: Apply design thinking to industrial problems, ensuring our AI solutions solve the right operational workflows (e.g., maintenance scheduling, process stability).

Key Skills

• Velocity-First Development: Expert proficiency in Cursor/Antigravity or similar AI-integrated IDEs to bypass boilerplate and build integrated features at 10x speed.

• The DS Stack: Strong foundation in Python, Polars/Pandas, and Scikit-learn. You should be comfortable enough with ML to tweak a model or optimize a RAG prompt.

The rest of this description is on the employer’s own page.