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Job Location: Gurgaon
What do we do at BluePi: BluePi is a born in cloud organization with one of the main focus area being Big Data and Analytics. At BluePi we solve different business problems for clients from different industrial domains like Media, Retail, Logistics, Clothing, etc. We at BluePi use different data sets and algorithms to build large scale solutions for clients that help facilitate and speed up decision making, enhance user experience and increase revenue. Our focus area is Natural Language Processing and Generation, Forecasting, Personalisation and Recommendations, Image processing and Generation, etc.
Roles and Responsibilities: As a Data Scientist at BluePi, you will :
1. Analyse and extract insights from large datasets. Perform feature analysis and extraction to give appropriate results using the different models.
2. Solve varied business problems like Real-time Recommendations, Sales Forecasting, Inventory Optimization, Dynamic Pricing, Entity Extraction, Text Analysis, Image Generation given a set of features, etc.
3. Work with different distributed technologies like Spark, Storm, Hadoop, etc and various machine learning/deep learning framework in distributed environments like Tensorflow, Keras, Pytorch, etc.
Skills Required:
1. Degree in Applied Mathematics, Statistics, Computer Science, or other quantitative fields. MS/PhD preferred.
2. 2+ years of experience in quantitative data analysis and employing various machine learning and deep learning models to solve business problems.
3. Understanding of the mathematical concepts behind the various algorithms used.
4. Demonstrated skills in selecting right statistical tool and algorithm, given a business problem.
5. Experience in working with large data sets using distributed technologies like Spark, Hadoop, Storm, etc. Having a good knowledge of SQL.
6. Good understanding of using various machine learning frameworks and toolkits like Scikit-learn, R, Spark MLib, tensorflow, keras, etc.
7. Experience in productionizing the machine learning/deep learning models would be an added advantage.
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