Data Scientist - Analytics - Global Data Insight Analytics

The Global Data Insights and Analytics (GDI&A) department - looking for qualified people who can develop scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization. The goal of GDI&A is to drive evidence-based decision making by providing insights from data. Applications for GDI&A include, but are not limited to, Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Warranty Analytics.

- Potential candidates should have hands-on experience in applying machine learning, data mining, and text mining techniques to build analytics prototypes that work on massive datasets.

- Candidates should have experience in manipulating both structured and unstructured data in various formats, sizes, and storage-mechanisms. 

- Candidates should have excellent problem solving skills with an inquisitive mind to challenge existing practices. 

- Candidates should have exposure to multiple programming languages and analytical tools and be flexible to using the requisite tools/languages for the problem at-hand.

Key Roles and Responsibilities of Position :

- Apply machine learning, data mining and text mining techniques to create scalable solutions for business problems

- Train, tune, validate, and monitor predictive models

- Analyze and extract relevant information from large amounts of historical business data, both in structured and unstructured formats

- Establish scalable, efficient, automated processes for large scale data analyses

- Package and present the findings and communicate with large cross-functional teams

Qualifications :

- M.S. or Ph.D. in Computer Science, Operational research, Statistics, Applied mathematics, or in a related field

- Experience with NoSQL database and Hadoop Ecosystem

- Good exposure to machine learning/text mining tools and techniques such as Clustering/classification/decision trees, Random forests, Support vector machines, Deep Learning, Neural networks, Reinforcement learning, and other numerical algorithms

- Knowledge of Spark, MLlib, Graph databases is a Big Plus

- Familiarity with any one programming (e.g., C/C++/Java) or scripting (R/Python) languages

- Excellent problem solving, communication, and data presentation skills

- Engineering or manufacturing or automotive background preferred

- Experience in product and sustainability analytics is a big plus.

Sneha
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