Machine Learning Engineer

Leading Multinational Co

Machine Learning Engineer

SIlverPeople Consulting

Company

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Machine Learning Engineer

at Leading Multinational Co

Agency job

via SIlverPeople Consulting

2 - 9 yrs

₹8L - ₹27L / yr

Mumbai

Skills

Machine Learning (ML)

ML Engineer-Analyst/ Senior Analyst

Job purpose:

To design and develop machine learning and deep learning systems. Run machine learning tests andexperiments and implementing appropriate ML algorithms. Works cross-functionally with the Data Scientists, Software application developers and business groups for the development of innovative ML models. Use Agile experience to work collaboratively with other Managers/Owners in geographically distributed teams.

Accountabilities:

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 degree in Engineering /Computer Science/ Math/ Statistics or equivalent. Master's degree in relevant specification will be first preference

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

ML Engineer-Analyst/ Senior Analyst

Job purpose:

Accountabilities:

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 degree in Engineering /Computer Science/ Math/ Statistics or equivalent. Master's degree in relevant specification will be first preference

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

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