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.

Work arrangement
Yes