Data Scientist

December 14, 2021
Application deadline closed.

Job Description

Experience: 5-10 years

Location: Bangalore

Salary: 6-18LPA

Skills preferred:

Minimum 5 years experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc. 

  • >5 years Experience querying databases and using statistical computer languages: R, Python, SQL, etc. 
  • Experience using web services: Redshift, S3, Spark, DigitalOcean, etc. 
  • Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc. 
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc. 
  • Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc. 
  • Experience in OCR and Face Recognition algorithms 
  • Strong understanding of text preprocessing and normalization techniques, such as tokenization, POS tagging, and parsing 
  • Should know the following frameworks: TensorFlow, PyTorch, Caffe, mxnet, Keras and Theano. 

Job Requirements:

  • 5-10 years of experience manipulating data sets and building statistical models  
  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies. 
  • Assess the effectiveness and accuracy of new data sources and data gathering techniques. 
  • Develop custom data models and algorithms to apply to data sets. 
  • Use predictive modelling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes. 
  • Coordinate with different functional teams to implement models and monitor outcomes. 
  • Develop processes and tools to monitor and analyze model performance and data accuracy. 
  • Strong problem-solving skills with an emphasis on product development. 
  • Experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets. 
  • Experience working with and creating data architectures. 
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks. 
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications. 
  • Excellent written and verbal communication skills for coordinating across teams. 
  • A drive to learn and master new technologies and techniques.