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
Shubham Vishwakarma
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