Data Scientist

Data Scientist Overview

• The Data Scientist role focuses on advancing predictive and prescriptive intelligence for industrial applications.
• Collaborate with R&D teams to develop cutting-edge AI solutions for predictive maintenance.
• Utilize expertise in time-series modeling, reinforcement learning, and knowledge representation.
• Work remotely in a dynamic environment with cross-functional teams.
• Contribute to publications, patents, and open-source projects to advance industry standards.
• Mentor junior data scientists and integrate research outputs into customer-facing products.
• Drive innovation by applying graph-based learning and multi-modal context fusion.
• Leverage deep-learning frameworks and modern ML techniques to optimize performance.

Data Scientist Key Responsibilities & Duties

• Develop and adapt deep-learning models for time-series analysis on industrial sensor data.
• Design reinforcement-learning formulations to recommend operational actions with confidence.
• Extend domain knowledge graphs for industrial assets and failure modes.
• Fuse multi-modal context into predictive pipelines for improved generalization.
• Collaborate with product and engineering teams to integrate AI outputs into workflows.
• Translate state-of-the-art research into production-grade implementations.
• Define and instrument quality metrics for predictive and prescriptive model outputs.
• Drive measurable accuracy gains across asset-class specific models.

Data Scientist Job Requirements

• PhD in Computer Science, Statistics, Electrical Engineering, or related discipline strongly preferred.
• 5+ years of applied machine-learning experience with expertise in time-series modeling, reinforcement learning, or graph-based learning.
• Proficiency in Python and deep-learning frameworks like PyTorch.
• Experience with cloud platforms and managed ML services.
• Strong software engineering practices including version control and reproducible experimentation.
• Domain exposure to predictive maintenance or industrial AI applications is a plus.
• Publications at top ML venues or relevant industry conferences are advantageous.
• Comfort with ambiguity and prioritization in a fast-paced environment.