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| Job Location | Bangalore |
| Education | Not Mentioned |
| Salary | Not Disclosed |
| Industry | Banking / Financial Services |
| Functional Area | General / Other Software |
| EmploymentType | Full-time |
ResponsibilitiesResearch and develop innovative ML based solutions to some of the firms hardest problemsUnderstand the business to formulate relevant high impact business questions that can be answered through data analysis.Work on building robust Data Science capabilities which can be scalable across multiple business use cases.Collaborate with Tech partners to design and deploy Machine Learning services that can be integrated with strategic systems.Think strategically to leverage reusable components within the firm and state of the art available externally when building the capabilities.Research and analyse data sets using a variety of statistical and machine learning techniques.Communicate final results and give context.Document approach and techniques used.Required Technical Qualifications and experience4-6 years experience in a reputable work environment.BTech, MS or PhD in a quantitative or computational disciplineHands-on experience developing and deploying Data Science and ML capabilities in production at scale.Strong ability to develop and debug in Python or similar professional programming language.Should be able to work both individually and collaboratively in teams, in order to achieve project goals.Must be curious, hardworking and detail-oriented, and motivated by complex analytical problems.Experience with Natural Language Processing (NLP).Able to be results and client focused and follow the agile development paradigm.Nice to HaveAble to design or evaluate intrinsic and extrinsic metrics of your model s performance which are aligned with business goals.Able to independently research and propose alternatives with some guidance as to problem relevance.Able to work with non-specialists in a partnership model, conveys information clearly and creates a sense of trust with stakeholders.Strong experience with machine learning APIs and computational packages (examples: Scikit-Learn, NumPy, SciPy, Pandas, statsmodels).Previous experience with Deep Neural Network packages (examples: TensorFlow, Theano, PyTorch, Keras).Experience with big-data technologies such as Hadoop, Spark, SparkML, etc.,
Keyskills :
natural language processingdata sciencemachine learningnatural languageagile developmentlanguage processingtechnical qualificationssparkagilehadooppythondesignsciencemetricsresearch