Data Scientist

Insight Global seeking a skilled Data Scientist to join our team and help us leverage data to prevent derailments and improve overall train operations.

Job Summary: As a Data Scientist specializing in Machine Vision and Sensor Analytics, you will be responsible for analyzing large datasets from various sensors to detect early-stage issues in wheel integrity and other critical components. Your work will involve developing new alarm types and composite alarms by studying trends and correlations in the data, ultimately enhancing the safety and reliability of our rail systems.

Key Responsibilities:
• Utilize machine vision systems to detect cracks and other anomalies in train wheels, particularly in the early stages of formation.
• Analyze sensor data to detect wheel impact events, temperature fluctuations in brakes and bearings, and measure the thickness of the metal on wheels.
• Study long-term data trends to build new alarm types that alert the team when images or sensor data indicate potential issues.
• Develop composite alarms by integrating information from multiple sensors over time.
• Collaborate with engineering and maintenance teams to implement data-driven solutions that prevent derailments and other safety incidents.
• Turn volume adjustments to balance between detecting critical issues and avoiding false alarms.

Experience & Qualifications:
• Proficiency in Python programming language.
• Experience with big data technologies such as Databricks and IBM Db2.
• Strong skills in data visualization using tools like Tableau (on-premises) and Power BI.
• Knowledge of machine learning tools and techniques for predictive analytics and anomaly detection.
• Experience with on-premises Db2 databases.
• Ability to load and process data in Power BI for comprehensive analysis.

Preferred Qualifications:
• Background in transportation or rail systems.
• Familiarity with sensor technology and machine vision systems.
• Strong problem-solving skills and attention to detail.
• Excellent communication skills to present findings to non-technical stakeholders.