Analyst-Data Science
American Express Bengaluru, Karnataka, India
Job Description
"Unlock the secrets of data-driven decision making as an Analyst-Data Science at American Express, where a single decision can impact millions."
In the Credit and Fraud Risk (CFR) team at American Express, data science meets business acumen to drive profitable growth, reduce fraud risk, and maintain industry-leading credit loss rates.
As an Analyst-Data Science, you'll be at the forefront of leveraging data and digital advancements to improve risk management, enable commerce, and drive innovation.
Why you should learn this:
With the increasing importance of data-driven decision making, the demand for skilled data science professionals is skyrocketing, with a projected growth rate of 14% in the next 5 years.
Expected Salary: The average salary for an Analyst-Data Science at American Express ranges from $120,000 to $180,000 per year, depending on experience and location.
How it works:
- Develop and maintain advanced data models to predict credit risk and detect fraud
- Collaborate with cross-functional teams to design and implement data-driven solutions
- Analyze large datasets to identify trends and insights that inform business decisions
Core Concepts to Master
Predictive Modeling
Develop and deploy machine learning models to predict credit risk and detect fraud, using techniques such as logistic regression, decision trees, and clustering.
Data Visualization
Create interactive and dynamic visualizations to communicate complex data insights to stakeholders, using tools such as Tableau, Power BI, and D3.js.
Statistical Analysis
Apply statistical techniques to analyze large datasets, identify trends and patterns, and inform business decisions, using tools such as R, Python, and SQL.
Interview Questions (Beginner)
- What is your experience with machine learning and predictive modeling?
- How do you approach data visualization and communication?
- What statistical techniques have you used to analyze large datasets?
Job Overview
Advance Questions
- • Can you describe a time when you had to develop and deploy a machine learning model in a production environment?
- • How do you handle imbalanced datasets and feature engineering?
- • Can you walk me through your process for creating a data visualization dashboard?