Brief description :
Data Scientist in Data Science CoE follow multiple approaches for project execution. He/she is expected to drive a business problem right from interacting with business, data collection, building a concept application to demonstrate the advantages of the new methodology with explanations. Upon successful demonstration he/she brings in original ideas, collaborate and work with team and 3rd parties for speed of delivery to solve and deploy the solution globally. They also leverage the vast global network of Nissan Motors to collaborate with Nissan Digital – Data Engineering CoE, Software CoE, CI/CD team and other functions for creating and deploying solutions.
· Overall years of experience: - 2-4yrs. of experience in Statistical and Machine learning models.
· Proficient in Python/R frameworks for machine learning.
: Proficient in Data Visualisation in python / R or using BI Tools such as Tableau
Proficient in Statistical modelling techniques and Machine Learning Algorithms
Proficiency in Optimization techniques with open source tools is a plus
· Should understand CI/CD processes in product deployment and used it in delivery.
· Should have understanding of Dockerization, REST APIs
· Working knowledge on following agile practices in product development is a plus
· Experience in various statistical and machine learning models, data mining, unstructured data analytics in corporate or academic research environments
· Proven background in at least one of the following - Reliability models, Markov Models, Stochastic models, Bayesian Modelling, Classification Models, Cluster Analysis, Neural Network, Non-parametric Methods, Multivariate Statistics
Ability to translate domain problems to data science problem
· Ability to think creatively to solve real world business problems
· Ability to work in a global collaborative team environment
· Proficient verbal and written communication skills in English
Applied experience in Operations Research, Statistical Modelling, Optimization with OR Tools or equivalent, PyTorch, Deep Learning, H2O.ai, Tensor Flow 2.0, Scikit Learn, Python, R, Julia, Numba, Cython, Spark. Django