Reliability Data Scientist

8 months ago

Chennai, India Ford Motor Company Full time

M
- Sc (PhD preferred) in reliability engineering, statistics, operations research, mathematics, or related fields R

eliability/survival analysis N
- onparametric (Kaplan-Meier) estimation P
- arametric distribution analysis o Proportional hazard modeling A
- ccelerated life test (ALT) and degradation data analysis S
- urvival regression analysis R

epairable system & recurrence data analysis N
- onparametric CIF procedures R
- enewal Process, NHPP, G-Renewal Process R
- epair Effectiveness Estimation M
- TBF modeling M

achine Learning Methods in Reliability Data Analysis R
- egression Algorithms (e.g., Deep Survival Cox Regression) R
- egularization Algorithms (e.g., Clustering Algorithms) D
- ecision Tree Algorithms (Survival Trees) A
- rtificial Neural Networks and Deep Learning Algorithms B
- ayesian Algorithms A

pplied Statics Methods M
- onte Carlo methods M
- ultiple regression analysis (GLM, Logistic regression, etc.) P
- CA and partial least squares, o Statistical design of experiments and ANOVA S
- tatistical process control (process capability, control charts) P

referred Operational Skills: C
- lose familiarity with the following corporate software systems: AWS, Data Cubes, SAS, SAS-JMP, Minitab. E
- xperience in Python and PySpark programming to feed into an ecosystem of modules and functions for advanced reliability and warranty analytics projects A
- bility 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

F
- ailure rate modeling & prediction S
- tatistical sampling strategies for field risk containment R
- ecall decision support & spare stock allocation D
- esign reliability support (design alternative/improvement evaluation, lab to field reliability correlation, reliability target demonstration [design life confirmation], root cause analysis) W
- arranty data analysis and early detection of emerging reliability concerns S

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