New Offcampus Job Opportunity from 10times is Hiring for the role of Data Scientist, Apply Now. Apply for the job as soon as possible.




10times is Hiring for the role of Data Scientist















Job Description:-

10Times is the world's leading platform for B2B event finding and networking. We are changing how people locate, interact, and engage at events such as trade exhibitions and conferences. We are seeking a Data Scientist to join our expanding engineering team. We are now analysing hundreds of millions of rows of data aimed at business experts in order to solve a few crucial business usecases. 

Do you enjoy working with enormous amounts of structured and unstructured data, discovering new data sources, developing attractive data models, and discovering business insights? Then continue reading!

Job Responsibilities:-

To solve crucial business problems, use or create models based on data.
Work with huge, complicated data sets to derive insights using excellent database abilities.
Convert unstructured data into useable datasets for use in improving business operations.
Find new internal and external data sources to assist with analytics activities. 
Create tools for automating repetitive requests in order to save up bandwidth for analytics.
Work with product managers to develop practical, high-impact insights and solutions.
Collaborate closely with upper management to prioritise information and analytic requirements.

Required Skillsets:-

Bachelor's or Master's degree in a quantitative subject (such as Engineering, Statistics, Math, Economics, or Computer Science with Modeling/Data Science), preferably with 2+ years of work experience.
Proven problem-solving and debugging abilities.
Knowledge of database technology and tools (SQL/R/SAS/JMP, for example), data warehousing, transformation, and processing. 
Work experience with real data for consumer insights, company and market analysis will be desirable.
Statistical expertise in standard techniques such as logistic regression, classification models, cluster analysis, neural networks, random forests, ensembles, and so on.

 



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