Machine Learning Engineer

at Leading Multinational Co

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Mumbai
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2 - 9 yrs
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₹8L - ₹27L / yr
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Full time
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

 

 

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