Data Scientist - Chennai, India - Ford Motor Company

Ford Motor Company

Verified Company

Chennai, India

1 week ago

Posted by:

Deepika Kaur

beBee Recuiter

Description

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 reliabilityrelated 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