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LTI Invites for Data Scientist Interview May @ Hyderabad

6.00 to 11.00 Years   Hyderabad   18 Jun, 2019
Job LocationHyderabad
EducationNot Mentioned
SalaryNot Disclosed
IndustryIT - Software
Functional AreaGeneral / Other SoftwareMiddleware
EmploymentTypeFull-time

Job Description

Greetings from LnT Infotech (larsen & toubro) !!We are hiring Data Scientist Role for F2F discussion on 18th May @ Hyderabad Location.

  • Total Experience - 6 Years to 12 Years
  • Relevant Experience in Data Science +ML - 5+ Years
  • Interview Date and Location- 18th May, 2019 at Hyderabad
  • Job Location- Hyderabad, Bangalore,Pune,Chennai
  • 6 to 12 years experienced data scientist Expert in Python Experience in Machine Learning Natural Language Process Text Analytics Statistical Analysis R Programming is added advantage Experience using statistical computer languages (Python, R, SLQ, 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.
  • Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
HOW TO APPLY
  • If interested, then share you updated CV to Goutham.kesham@lntinfotech.comalong with below details.
1.Full Name: 2.Mob No: 3.Email ID: 4.Education: 5.Total Exp: 6.Rel. Exp in Data Science-7.Rel.Exp in Python (Need 5+ yrs)- 8.Current CTC: 9.Exp CTC: 10.Notice Period:
  1. Available for FTF Interview on 18th May (Yes/No)-

Keyskills :
nlppythonecisiontreelinearregressionnaturallanguageprocessingdatascientistregressionanalysismachinelearninglogisticregressionpredictivemodeling

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