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| Job Location | Bangalore |
| Education | Not Mentioned |
| Salary | Not Disclosed |
| Industry | Recruitment Services |
| Functional Area | General / Other Software |
| EmploymentType | Full-time |
You have a strong academic background in statistics and machine learning. The typical candidate has a Bachelors or Masters degree in Math, Statistics, Computer Science, Physics or such quantitative fields or has done a program from a business school in marketing, analytics etc. with a focus on quantitative approaches.Overall 6+ years with most of your experience were related to data and data analysis. You have worked on a variety of complex data analysis and modeling problems, gathering a great deal of practical wisdom on how to apply these techniques to real world scenarios.You are competent enough to roll up your sleeves and get things done as a data scientist when the situation demands. You have a wide range of statistical and machine- learning tools under your belt. These include linear models for regression and classification, multi- level models, factor analysis PCA, discriminant analysis, support vector machine, decision tree ensembles bootstrap, neural networks, mixture models clustering algorithms, and so on. You are proficient in at least one programming language commonly used for data analysis (like R/Python), and you are cozy with SQL.Previously worked on business analytics problems like customer churn, lifetime value estimation, targeted marketing, personalized offers, etc. And experienced in designing analyzing controlled experiments for targeted interventions in the fieldJob Requirements:Masters or Doctorate in relevant domain.Knowledgeable on different database and data warehousing systems like MySQL, Amazon Redshift, BigQuery, TeradataExperienced in working with large data sets, with big data processing tools like MapReduce, Spark, Hive, etc. Have data engineering skills to do preprocessing, cleaning and transformations.Possess strong data visualization skills using programmatic tools (e.g. ggplot2, shiny, d3.js) and other visualization frameworks like victory, highcharts etc.,
Keyskills :
linear modelscomputer science data warehousingteradata sql neural networksmachine learning lifetime valuedata engineering