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Fraud Manager

3.0 - 7.0 years

Hyderabad, All india

On-site

Role Overview: You will be joining EY's Financial Services Office (FSO) as a Senior Consultant in the Financial Services Risk Management (FSRM) group, specifically focusing on Credit Risk. In this role, you will work with a diverse range of financial institutions and banking organizations to design and implement strategies to manage credit risks associated with their banking book portfolios. Key Responsibilities: - Demonstrate deep technical knowledge and industry expertise in financial products, particularly lending products. - Lead various components of client engagements, ensuring the delivery of high-quality services. - Stay updated on market trends and client challenges within the finan...

Posted 1 week ago

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Exploring Model Development Jobs in India

The field of model development in India is experiencing rapid growth, with an increasing demand for professionals who can build and enhance predictive models for various industries. From finance to healthcare to e-commerce, companies are seeking skilled individuals who can leverage data to drive business decisions and outcomes.

Top Hiring Locations in India

Bengaluru
Mumbai
Delhi
Hyderabad
Pune

Average Salary Range

The average salary range for model development professionals in India varies based on experience and location. Entry-level positions may start around INR 4-6 lakhs per annum, while experienced professionals can earn upwards of INR 15-20 lakhs per annum.

Career Path

In the field of model development, a typical career progression may include roles such as Junior Developer, Data Scientist, Senior Developer, and eventually, Tech Lead. As professionals gain experience and expertise, they may also take on managerial or leadership positions within organizations.

Related Skills

In addition to model development, professionals in this field are often expected to have skills in: - Data analysis - Machine learning - Programming languages (e.g., Python, R) - Statistical modeling - Data visualization

Interview Questions

What is the difference between supervised and unsupervised learning? (basic)
Explain the bias-variance tradeoff in machine learning. (medium)
How do you handle missing data in a dataset? (basic)
Can you explain the concept of overfitting in machine learning models? (medium)
What is the purpose of regularization in model development? (medium)
How would you evaluate the performance of a classification model? (medium)
Can you discuss the difference between precision and recall? (medium)
What is cross-validation and why is it important in model development? (medium)
How would you select the right algorithm for a given machine learning problem? (medium)
Explain the concept of feature engineering in model development. (medium)
What is the difference between a decision tree and a random forest? (medium)
How would you handle imbalanced classes in a classification problem? (medium)
What is dimensionality reduction and when would you use it in model development? (medium)
Can you explain the concept of AUC-ROC curve in evaluating classification models? (advanced)
How do you interpret coefficients in a linear regression model? (medium)
What is the purpose of gradient descent in machine learning? (medium)
Can you discuss the difference between L1 and L2 regularization? (medium)
How does K-means clustering work? (medium)
What is the importance of feature scaling in machine learning models? (basic)
Explain the concept of ensemble learning and provide examples. (medium)
How do you handle multicollinearity in regression models? (medium)
Can you discuss the bias-variance tradeoff in the context of model complexity? (medium)
What is the purpose of hyperparameter tuning in machine learning? (medium)
How would you explain the concept of a support vector machine to a non-technical audience? (medium)
Can you discuss the difference between classification and regression models? (basic)

Closing Remark

As you explore opportunities in model development in India, remember to showcase your expertise, skills, and passion for data-driven decision-making. Prepare thoroughly for interviews, demonstrate your knowledge and experience, and apply confidently to take the next step in your career journey. Good luck!

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