About the Company :
A global conglomerate operating in 200+ countries having turnover of 50+ billion dollars is the industry's global leader, providing rapid, reliable, time-definite delivery to more than 220 countries and territories
Work with Data Scientists and Business Analysts to frame problems in a business context. Assist all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production.
Understand business objectives and developing models that help to achieve them, along with metrics to track their progress.
Explore and visualize data to gain an understanding of it, then identify differences in data distribution that could affect performance when deploying the model in the real world.
Define validation strategies, preprocess or feature engineering to be done on a given dataset and data augmentation pipelines.
Analyze the errors of the model and design strategies to overcome them.
Collaborate with data engineers to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production and demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.
Qualifications & Specifications :
Bachelor's / Master's degree in Engineering / Computer Science / Math / Statistics or equivalent.
Experience of machine learning algorithms and libraries
Understanding of data structures, data modeling and software architecture.
Deep knowledge of math, probability, statistics and algorithms
Experience with machine learning platforms such as Microsoft Azure, Google Cloud, IBM Watson, and Amazon
Big data environment : Hadoop, Spark
Programming languages : Python, R, PySpark
Supervised & Unsupervised machine learning : linear regression, logistic regression, k-means clustering, ensemble models, random forest, svm, gradient boosting
Sampling data : bagging & boosting, bootstrapping
Neural networks : ANN, CNN, RNN related topics
Deep learning : Keras, Tensorflow
Experience with AWS Sagemaker deployment and agile methodology