Data Scientist - Python & Machine Learning - BFS

Position - Data Scientist

Location - Gurgaon & Bangalore

Job Responsibilities :

You will be on top of the development of machine learning models/predictive analytics techniques that optimizes processes and reinventing customer engagement methods. In addition, you will have an opportunity on working through big data problem from data mining, data cleaning, building concept of the model to support decision making through various data analytic techniques and algorithm development involving unstructured and structured data. 

You will perform research on smart concepts and customer facing applications. You will be involved in technical planning, execution and delivery of development projects for global industry members. You will write technical reports and present technical results to internal teams and industry at large.

Desired Competencies :

- Ph.D. or master's in computer science, B. Tech/ B.E. /M. Tech. Graduate in Computer Science, Operations Research, Industrial Engineering or Mathematics / Statistics related fields is preferable.

- 4-7 yrs. or relevant work experience.

- Understanding/experience in NLP, NN and Computer Vision

- Fluent in the programming languages - Python/R & SAS

- Adept at Machine Learning Concepts - The deeper, the better. (SVM, Random Forests, Logistic Regression, Bagging and Boosting, Linear and Nonlinear models, Bayesian theory, Recommendation systems etc.)

- Working knowledge of a query language SQL/MySQL.

- Knowledge of visualization tools like R-Shiny/Matplotlib

- Experience in the development of ML models at scale and taking them into the production environment is a big plus.

- Deep understanding of statistical and predictive modelling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms 

- Strong understanding of predictive modelling algorithms such as logistic regression, neural networks, SVM and decision trees

- Experience in building ML models at scale, using real-time big data pipelines on platforms such as Spark/MapReduce