Data Scientist

1 week ago

Chennai, India Ford Motor Company Full time

Failure rate modeling & prediction - Statistical sampling strategies for field risk containment - Recall decision support & spare stock allocation - Design reliability support (design alternative/improvement evaluation, lab to field reliability correlation, reliability target demonstration [design life confirmation], root cause analysis) - Warranty data analysis and early detection of emerging reliability concerns - Statistical warranty forecasting and warranty reserve modeling

MSc (PhD preferred) in reliability engineering, statistics, operations research, mathematics, or related fields
- Reliability/survival analysis
- Nonparametric (Kaplan-Meier) estimation
- Parametric distribution analysis
- Proportional hazard modeling o Accelerated life test (ALT) and degradation data analysis
- Survival regression analysis
- Repairable system & recurrence data analysis
- Nonparametric CIF procedures
- Renewal Process, NHPP, G-Renewal Process
- Repair Effectiveness Estimation
- MTBF modeling
- Machine Learning Methods in Reliability Data Analysis
- Regression Algorithms (e.g., Deep Survival Cox Regression)
- Regularization Algorithms (e.g., Clustering Algorithms)
- Decision Tree Algorithms (Survival Trees)
- Artificial Neural Networks and Deep Learning Algorithms
- Bayesian Algorithms
- Applied Statics Methods Monte Carlo methods
- Multiple regression analysis (GLM, Logistic regression, etc.)
- PCA and partial least squares,
- Statistical design of experiments and ANOVA
- Statistical process control (process capability, control charts)
- Preferred Operational Skills:

- Close familiarity with the following corporate software systems: AWS, Data Cubes, SAS, SAS-JMP, Minitab.
- Experience in Python and PySpark programming to feed into an ecosystem of modules and functions for advanced reliability and warranty analytics projects
- Ability to work across diverse teams in GDIA and global business organizations to drive reliability-related initiatives and manage data pipelines across a variety of projects with the goal of creating a seamless user experience for the suite of reliability, warranty and quality tools

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