Mercedes-Benz Research & Development India Headquartered in Bengaluru was founded in 1996 as a captive unit to support Daimler’s research, IT and product development activities. We focus on topics ranging from computer-aided design and simulations (CAD, CAE) for powertrain, chassis and exteriors to embedded systems, telematics and developing various IT applications and tools. The satellite office in Pune specializes in interior component designs and IT engineering. It is now one of the largest global R&D centers outside Germany, employing more than 3000 + skilled engineers. It aims to partner closely with suppliers in India for its activities in product development and IT services.
We are an equal opportunity employer and value diversity at our company.
As a Data scientist – Reliability and Mathematical Optimization you will :
- Build mathematical programming models: Linear Programming, Mixed Integer Programming, Network Optimization.
- Develop Optimization algorithm design and implementation.
- Generate Synthetic data for massive simulation situations.
- Adapt reliability and statistical tools to complex situations involving minimal information and data.
- Develop and implement simulation models, predictive reliability tools and statistical models for failure forecasting.
- Work closely with Global teams to participate in design reviews and engineering team meetings.
- Master in Statistics/Economics or MBA or M. Sc./M. Tech with Operations Research/Industrial Engineering/Supply Chain
- Minimum 5+ years of analytics experience in automobile, manufacturing or related domain.
- Expertise in stochastic and deterministic optimization techniques and simulation
- Experience in Reliability/Survival analysis, such as: non-parametric (Kaplan-Meier) estimation, parametric distribution analysis, proportional hazard modeling, accelerated life test and degradation data analysis, survival regression
- Experience with probabilistic modeling (e.g. Monte Carlo simulation).
- Hands-on experience in delivery of projects using statistical modelling.
- Experience in reliability engineering and applied statistics (DOE, regression, life data analysis etc)
- Hands on experience with Machine learning, data analysis, applied statistics, deep learning knowledge and experience.
- Excellent programming skills and proficiency in Python and Pyspark (Must)
- Experience in simulations and predictive analytics with Matlab.
- Distributed computing experience with Hadoop development in HIVE, Spark/Databricks etc.
- Ability to translate business requirements to problem definitions, models, and constraints
- Fluent with full development cycle, continuous integration and continuous deployment, version control tools.
- Applicants with Automobile / EV domain will be preferred.