Data Scientist

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Skills you'll use

What you'll do

• 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).
• 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.
• Industrial Intuition: You "get" the domain—you understand that a 2% drift in a sensor might be a critical failure or just noise, and you know how to design a UI that communicates that nuance.
• Full-Stack Data Ownership: You are comfortable managing the data flow from a PostgreSQL/TimescaleDB backend through a FastAPI layer and into a polished frontend.
• Product Sense: You can sit with a customer, look at their "messy" data, and sketch a solution that solves their pain point before the meeting ends

Who can apply

• 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).
• 3+ years of experience in a role that blended Data Science with Software Engineering (e.g., ML Engineer, Product Engineer, or DS Prototyper).
• A portfolio demonstrating end-to-end AI applications—we want to see things you’ve built that people have actually clicked on.
• Prior experience in Industrial AI, Manufacturing, or Energy is highly preferred.
• A "Hacker" mindset: You love finding the shortest path between a complex data problem and a working solution
• 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.

About UPTIMEAI TECH

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