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Sr. Data Scientist - Credit and Fraud

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2-6 Years
13,10,000 - 16,50,000 INR
a month ago
621 Viewed
102 Applied

Job Description

HIRING FOR INFOSYS

Responsibilities:
  • Work closely with product and operation teams to implement new fraud prevention practices using ML/DL.
  • Develop highly scalable fraud risk models and tools leveraging machine learning, deep learning, and rules-based models.
  • Work closely on due diligence and integration of 3rd party fraud prevention vendors.
  • Build state-of-the-art fraud risk models using alternative data such as device data, network data, etc.
  • Build various credit risk models (underwriting model, behavior risk model, propensity model, etc. ).
  • Build capabilities to automatically manage credit lines for users based on optimization techniques.
  • Own the Data Science model end-to-end, from data collection to model building, to monitoring the model in production.
  • Build Machine Learning and Deep Learning models in the customer lifecycle which include Personalization, Recommendation, Rewards, Referrals, Transaction Categorization, and Customer Science-related models.
  • Understands the End to End ML pipeline.

Requirements:
  • Bachelor's or Master's degree in Computer Science, Information management, Statistics, or a related field, with 2 to 6 years of relevant work experience.
  • Experience in risk specifically in credit or fraud risk at alternative lending, buy-now-pay-later, payment, credit card, or top-tier consultancy companies.
  • Python programming skill is a must. Strong coding capabilities in ML and Deep learning.
  • Experience in statistical modeling, machine learning, data mining, unstructured data analytics, and natural language processing.
  • Sound understanding of - Bayesian Modeling, Classification Models, Cluster Analysis, Neural Networks, Nonparametric Methods, Multivariate Statistics, etc.
  • Familiarity with basic ML Engineering concepts, and understanding of OOP programming concepts.
  • Strong in data analysis and data wrangling. Experience with common libraries and frameworks in data science.
  • Familiarity with database queries and data analysis processes (SQL, Python).
  • Outstanding leadership, influencing, communication, interpersonal, and teamwork skills.
  • Detail-oriented, with the ability to work both independently and collaboratively.
  • Any prior research publication is a plus point.
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