Job Location: United States
Data Analytics Contractor Internship
Provide data analytics and modeling to support Engineering, Operations, Quality and Maintenance Planning teams.
Duration: one to two years, part-time ~20 hours/week during the school year and possibly more hours when school is not in session
Responsibilities:
Data Engineering / Automation
- Partner with IT and other subject matter experts to develop foundational, robust, high quality, integrated data sets to be used for visualization and advanced analytics
- Continuous improvement and automation of manual or recurring analytical requests or dashboards
- Leverage available tools (i.e. SAS, Python, SQL) to streamline and automate processes
- Training and support to enable self-service analytics for internal customers
Data Science/ Advanced Analytics
- Leverage advanced analytics and simulation models to evaluate railcar asset health and effectiveness of maintenance programs, such as condition-based maintenance (CBM)
- Plan repair requirements at the system level by identifying the distribution of inspected and repaired components to support maintenance strategies
- Develop data models to assist with long-term repair capacity and production planning decisions
Descriptive Analytics/Data Visualization
- Evaluate component performance leveraging metrics such as failure rate and cost per mile
- Develop and maintain a suite of operational metrics to support repair operations in the field
- Create and maintain Tableau dashboard metrics to support the Engineering team
- Enhance and further develop existing Tableau dashboards and metrics
Reliability Analytics
- Provide statistical and financial analysis to evaluate the performance of railcar components to support Engineering, planning, and supplier decisions
- Support Engineering with data analysis by evaluating the performance of new designs or new manufacturers of components applied to TTX railcars
- Publish component reliability reports as part of TTX’s annual supplier evaluation process
- Calculate life cycle cost and generate life curves (survival curves and Weibull distributions)
Qualifications
- Major(s): Decision Sciences, Analytics, Computer Science, Statistics, Engineering or related majors
- Year in school: First year, graduate students preferred. Senior undergraduates continuing their education will also be considered.
- Software: SQL, SAS, Python, Tableau, and Advanced Excel skills (VBA, Macros, Power Query)
Desired Qualities
- Strong technical and analytical skills
- Highly skilled in data wrangling and troubleshooting problems
- Ability to tackle complex data integration challenges (multiple sources, large volume)
- Customer service perspective and desire to provide accurate, reliable analyses
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