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Kotak

Data Scientist_SUPPORT SERVICES_0789_1288_1081157

Kotak Mumbai, Maharashtra, India

Banking · 10,001+ employees

Oct 09
data-scientist Mid (2-5 yrs) Full-time India
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About the role

The Data Scientist will build and deploy machine learning models to optimize business processes and assess credit risk for the banking and insurance sectors. They are also responsible for identifying cross-sell and up-sell opportunities and managing the full lifecycle of analytical models.

What they look for

Machine learning Statistical modeling Predictive modeling Credit risk assessment Customer experience personalization Python SQL Data analysis Model deployment Version control Feature engineering Exploratory data analysis Cross-sell Up-sell Business process optimization

Requirements

Candidates are expected to have strong coding skills for model pipelines and experience in version control and configuration management. The role requires the ability to translate business needs into actionable data-driven insights and maintain robust, scalable models.

Full description

Role Data Scientist/Sr Data Scientist

Business: Not Applicable Department: Analytics Sub-Department: Not Applicable

Location: Mumbai

Role

As a Data Scientist specializing in to building Analytical Models for Banks/NBFCs/Insurance industry, your primary responsibility is to utilize machine learning algorithms or statistical models to optimize Business Processes across Customer Life Journey. You will be required to build the model pipeline, work on its deployment and provide the required support until its last leg of adoption. Your role involves collaborating with cross functional teams to develop predictive models that assess Credit Risk and/ or personalize Customer Experience. You will also be responsible for identifying relevant base for targeting (cross-sell/up-sell) and running these campaigns from time to time.

Key Responsibilities

1. Writing Optimized Code:

  • Develop and maintain efficient, scalable code for model pipelines
  • Implement best practices in coding to ensure performance and resource optimization

2. Version Control and Lifecycle Configuration:

  • Familiar with best practices to manage codebase changes with available resources
  • Maintain and update configuration files to manage the lifecycle of data science models

3. Extracting Usable Information from Data:

  • Translate business needs into data-driven solutions and actionable insights
  • Conduct thorough exploratory data analysis to uncover trends and patterns.
  • Create and maintain feature repositories to document and manage features used in models

4. Building Robust Models:

  • Develop models that are reliable and generalize well across different time periods and customer segments.
  • Continuously monitor and improve model performance to adapt to changing data and business conditions

5. Publishing Model Outcomes:

  • Communicate model results and insights effectively to stakeholders.
  • Facilitate the adoption of models by ensuring they meet business needs and are user-friendly.

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