CJ0005 - Data Scientist/ R-Shiny Developer

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Job Brief

You will be expected to play a key role in driving the future business road map of the company. We are looking to hire people who are looking to contribute, enhance their skills in latest technology areas and are ready to work in an extremely challenging and fun place to deliver results in very aggressive timeline.

  • Experience with common data science toolkits/frameworks like Python, R, R-Shiny, NumPy, Keras, Flask, scikit-learn, Tensorflow, Pandas, Numpy, Matplotlib, Seaborn, PyTorch.
  • Very good understanding of various modeling approaches, statistical techniques and implementation strategies.
  • Excellent understanding of machine learning techniques – Classification (SVM, Decision Trees, Naive-Bayes) and Regression (linear, polynomial) algorithms.
  • Expertise in data mining & text mining (NLP/NLU).
  • Demonstrate experience in unsupervised techniques (clustering and anomaly) and also in time-series analysis and Forecasting.
  • Hands-on expertise in Voting Classifiers, XG-boost, neural networks, k-nearest neighbours, K-Means, Bayers, LDA.
  • Experience in data visualization tools such as PowerBI, Tableau, D3.js, GGPlot etc.
  • Good applied statistics skills such as distributions, statistical testing, regression etc.
  • Understanding of Hadoop eco-system, Spark with Scala or Python.

Responsibilities

  • Deliver client ready analytics solution
  • Data understanding, extraction, merging data from multiple sources, data preparation and transformation
  • Exploratory data analysis employing large data sets including visualization
  • Identify, develop and implement statistical techniques and algorithms that address business requirements.
  • Create and implement data models.
  • Suggest the right algorithm/model to be used for specific business case.
  • Ability to read and apply ML research papers to solve business problems.
  • Top 3 Skills:
  • Experience with common data science toolkits/frameworks like Python, R, R-Shiny, NumPy, Keras, Flask, scikit-learn, Tensorflow, Pandas, Numpy, Matplotlib, Seaborn, PyTorch.
  • Excellent understanding of machine learning techniques – Classification (SVM, Decision Trees, Naive-Bayes) and Regression (linear, polynomial) algorithms.
  • Demonstrate experience in unsupervised techniques (clustering and anomaly) and in time-series analysis and Forecasting

Apply Here

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