Senior Data Scientist

Senior Data Scientist Overview

• The Senior Data Scientist will lead advanced predictive analytics and AI research, driving innovation in predictive maintenance solutions.
• Collaborate with cross-functional teams to integrate AI outputs into customer workflows and enhance operational decision-making.
• Utilize expertise in time-series modeling, reinforcement learning, and knowledge representation to develop cutting-edge solutions.
• Contribute to the development of scalable AI models tailored to industrial sensor data and operational constraints.
• Mentor junior team members and contribute to the organization’s research and development initiatives.
• Drive measurable improvements in predictive and prescriptive model accuracy and performance.
• Engage in cutting-edge research and apply findings to practical industrial applications.
• Work remotely with a dynamic team of professionals in a collaborative environment.

Senior Data Scientist Key Responsibilities & Duties

• Develop and adapt deep-learning models for analyzing time-series data from industrial sensors.
• Design and implement reinforcement-learning algorithms for operational decision-making and optimization.
• Extend domain knowledge graphs and apply graph-based learning for asset relationship analysis.
• Integrate multi-modal data sources into predictive and prescriptive analytics pipelines.
• Collaborate with engineering teams to deploy scalable AI solutions in production environments.
• Mentor junior data scientists and provide technical guidance to enhance team capabilities.
• Translate state-of-the-art research into practical, production-ready implementations.
• Define and monitor quality metrics for predictive and prescriptive models.

Senior Data Scientist Job Requirements

• PhD in Computer Science, Statistics, Electrical Engineering, or a related field; equivalent experience considered for exceptional candidates.
• 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 such as PyTorch.
• Experience with cloud platforms and managed machine learning services.
• Strong software engineering practices, including version control and reproducible experimentation.
• Excellent communication skills to convey technical concepts to diverse audiences.
• Familiarity with predictive maintenance and industrial AI applications is a plus.
• Publications in top machine learning venues or relevant industry conferences are advantageous.