Job Description

Senior Data scientist Responsibilities

Key Responsibilities

\xc2\xb7 Dealing with large amounts of data and extract knowledge or insight to

analyze fraud cases obtained from clients and flag off any inconsistencies.

\xc2\xb7 Proficiency in handling imperfections in data is an important aspect of this role

\xc2\xb7 Develop scientific ML Models, processes, to extract knowledge or insights to Perform data analysis to support and develop fraud prevention analytical models

\xc2\xb7 Research data patterns for prediction of fraudulent transaction

\xc2\xb7 Identify and improve the Fraud detection performance /Model delivery time of previously delivered models with the help of advanced computational algorithms and tools .

\xc2\xb7 Cross-validating models to ensure their generalizability.

\xc2\xb7 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.

\xc2\xb7 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

\xc2\xb7 Exceptional technical and business communication in English, verbal and written

Collaboration

\xc2\xb7 Collaborate with Business and other stockholders within Nice

\xc2\xb7 candidate should be apt to solve data problems in a simple and agile manner

Stakeholder Management

\xc2\xb7 Ability to explain complex analytical concepts to non-technical audience.

\xc2\xb7 Ability to effectively advocate technical solutions to customers / account managers /Data engineering teams and business audiences.

Innovation and knowledge sharing

\xc2\xb7 Present innovative solutions to various stakeholders and bring that skill within team to make them stay ahead.

\xc2\xb7 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.

\xc2\xb7 Self-motivated to participate in Nice wide various innovative forums.

Experience: 3 to 8 Years

Qualification and skills:

• Advanced degree in a quantitative area (statistics, mathematics, physics, computer science, engineering)
• Experience in application and knowledge of statistical techniques and concepts (statistical tests, regression, feature selection, time series modeling, etc.) Strong experience with using SQL and EXCEL.
• Strong programming skills in Python 3.7 or more and ability to rapidly learn new programming tools and experience in application and knowledge of machine learning techniques (clustering, decision trees, boosting, etc.) and their advantages or drawbacks.
• Good command into develops and deploying classification and regression machine learning models at an enterprise level.
• Versed with various bagging boosting methods and able to choose which one should be used based on data.
• Have fair understanding of Logistic regression and various regularization techniques.
• Having knowledge of Model deployment and production model monitoring with ML-ops framework or containerized environment (Kubernetes) will be plus.
• Expert into troubleshooting issues with a variety of production data and past deployed models.
• Familiarity with various cloud infrastructure (especially AWS and Azure) is a good to have.
• Ability to work in multi-disciplinary agile teams.
• Strong commitment to deliver work with \xe2\x80\x9cRight first time\xe2\x80\x9d approach

• Experience in development of risk management models, particularly in the fraud or financial compliance areas.
• Knowledge of national and international financial systems and appropriate data standards.
• Experience of containerized Model development using Kubernetes

NICE Systems