Senior Data scientist Responsibilities
Key Responsibilities
Dealing with large amounts of data and extract knowledge or insight to
analyze fraud cases obtained from clients and flag off any inconsistencies.
Proficiency in handling imperfections in data is an important aspect of this role
Develop scientific ML Models, processes, to extract knowledge or insights to Perform data analysis to support and develop fraud prevention analytical models
Research data patterns for prediction of fraudulent transaction
Identify and improve the Fraud detection performance /Model delivery time of previously delivered models with the help of advanced computational algorithms and tools .
Cross-validating models to ensure their generalizability.
Staying informed about developments in Data Science and adjacent fields to ensure that outputs are always relevant and drive the continuous improvement of Ways of Working of the Data Science and analytics.
Bring together disparate data types in compelling visualization packages that provide end users with the ability to see both \'the landscape\' and details leading to improved decision-making Presenting results in a clear manner
Exceptional technical and business communication in English, verbal and written
Collaboration
Collaborate with Business and other stockholders within Nice
candidate should be apt to solve data problems in a simple and agile manner
Stakeholder Management
Ability to explain complex analytical concepts to non-technical audience.
Ability to effectively advocate technical solutions to customers / account managers /Data engineering teams and business audiences.
Innovation and knowledge sharing
Present innovative solutions to various stakeholders and bring that skill within team to make them stay ahead.
Should be a good team player to actively contributes to team to complete tasks, meet goals, respect ideas and aim to improve the product or process at hand.
Self-motivated to participate in Nice wide various innovative forums.
Experience: 4 to 7 Years
Qualification and skills:
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