Data Science (ML)

Data Science (ML)

Company Name Confidential
Bengaluru / Bangalore Hyderabad / Secunderabad
10 - 12 Years
Not Specified

Job Description

Roles & Responsibilities

  • Knowledge and experience with statistical Natural Language Processing (NLP) methods and technologies is desired.
  • •Experienced in AI / ML projects using languages such as Python, R and Scala. •Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Regression, etc. •Experience with data science toolkits, such as Shiny, Trifacta, NumPy, Pandas, NLTK, Scikit-learn, SpaCy, etc. Excellence in Python is highly desirable. •Ability to pick up new tools / techniques in ever changing technology landscape is a default expectation.
  • •An enterprise experience demonstrating all the above is must. •Experience with big data analytics & Hadoop ecosystem of tools.
  • •Expertise in leveraging Azure cloud for analytics work loads using data bricks. •Undertake preprocessing of structured and unstructured data
  • •Experience of working with AI platform like DataRobot will be helpful. •Expertise in SQL much needed and ability to work with SQL server environments and exposure to SSIS will be added advantage. •Experience with data visualization tools, such as Power BI.
  • •Good understanding and experience of ML modeling lifecycle and pipelines, including data preparation, data wrangling, ML model development, model training, model performance measurement and tuning
  • Experience with NoSQL databases, such as MongoDB, Cassandra, HBase and data frameworks, such as Hadoop.
  • •Good applied statistics skills, such as distributions, statistical testing, regression, etc.
  • •Experience on deep learning frameworks, like Tensorflow, Keras, etc and experience in dealing with text and/or image data will be desirable.
  • •Demonstrated knowledge of end-to-end deployment solutions for data products
  • •Experience with data cleaning, preparation, feature engineering and model selection techniques • Good understanding of the risks and trade-offs involved in choosing different ML solutions
  • •Ability to seek empirical evidence through proofs of concept, statistical validation and external research • Experience with Cognitive Services on Azure/AWS.











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