Machine Learning Engineer
* 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.
* Experience of machine learning Algorithms and Libraries.
Roles & Responsibilities
* 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.
Aptitude Tests, Technical Tests, Interviews, Medical Health Checkup.
Best in Industry
Remote (Work From Home)