Philips is conducting an off-campus recruitment drive to hire as Intern-Data Analytics. Interested candidate can read the details and apply as soon as possible.
About: Koninklijke Philips N.V. is a Dutch multinational conglomerate corporation headquartered in Amsterdam, formerly one of the largest electronics companies in the world, currently focused in the area of health technology, with other divisions being successfully divested.
Position: Intern-Data Analytics
Qualification: B.E/ B.Tech/ M.Tech/ MBA
Salary: Rs 8-10 LPA
- Good undergraduate or Master degree in a mathematical or technical discipline, such as Mathematics, Economics, Computer Science, Engineering, MBA.
- Your passion is focused on the design of algorithms and products to solve real, pressing problems using data.
- Obsessed with data, very strong quantitative and analytical muscle.
- Have 1+ years of experience with predictive modeling and statistical analysis techniques in a business environment. Exceptional recent graduates will also be considered.
- Creates methods of obtaining data that can be used for data sciences; Identifies ways to improve current data mining for future recommendation to reduce manual manipulation of data.
- Adeptly interpret and utilize mass quantities of data to generate innovative hypotheses & insights, and present these insights to the different stakeholders.
- A strong passion for empirical research and for answering hard questions with data.
- Develop, maintain, track and identify fine-tuning opportunities for existing machine learning models for different business problems.
- Consider the full impact of your work. This means considering privacy, ethics, and regulation, as well as the performance of your code and the accuracy of your models.
- Good understanding of statistical and machine learning modeling concepts.
- Strong with R / Python programming or any other open source programming language.
- Good Exposure to data mining, web data scrapping and data wrangling using R / Python or any other open source programming language.
- Strong understanding and exposure to Natural Language Processing (NLP) and text data mining tasks a plus.
- Exposure to supervised / unsupervised classification techniques and approaches specific to Document clustering, Topic modeling and other NLP tasks.
- Experience and exposure in various data extraction methods (like data extraction from PDF Files, JSON, HTML and Java Script rendered web pages, etc.) a plus.