PGDM graduates specializing in business analytics have a wide array of career opportunities. They can delve into market research, data analysis, business intelligence, predictive analytics, and data science. These professionals are also well-suited for consulting and advisory roles, where they can leverage their analytical prowess and business acumen to deliver actionable insights and solutions to clients across diverse industries. Their expertise in data manipulation, statistical modeling, and business strategy enables them to drive data-driven decision-making and enhance organizational performance.
Lets Discuss Top 3 Career Path After MBA in Business Analytics
Role of Credit Risk Analytics
Credit Risk Analytics plays a critical role in financial institutions by assessing the risk associated with lending to individuals or entities. It involves the use of statistical models, machine learning techniques, and advanced analytics to predict the probability of default (PD) and other credit risk metrics. The primary objective is to minimize the risk of financial losses while maximizing profitability through informed decision-making and robust risk management practices.
Skills and Responsibilities
Technical Skills:
- Python, SQL, and SAS:
- Develop and deploy credit risk models using Python.
- Use SQL for database management and data extraction.
- Implement SAS for advanced statistical analysis and model validation.
- Statistical Modelling and Machine Learning:
- Build and validate models such as Logistic/Linear Regression, GBM, Decision Trees, Random Forest, and SVM.
- Use advanced techniques like K-Means Clustering, NLP/Text Analytics, and LSTM for complex data analysis.
- Data Exploration and Big Data Technologies:
- Perform exploratory data analyses to understand data patterns and insights.
- Leverage big data technologies like Hadoop and Spark for handling large datasets.
- Utilize cloud platforms such as AWS, Azure, and GCP for scalable analytics solutions.
Credit Risk and PD Model Development:
- Develop and validate Probability of Default (PD) models to predict the likelihood of borrowers defaulting.
- Continuously monitor and refine models to ensure accuracy and compliance with regulatory standards.
- Formulate and implement credit risk policies and risk appetite frameworks.
Marketing Analytics:
- Apply analytical techniques to understand customer behavior and improve marketing strategies.
- Optimize customer acquisition and retention by modifying existing rules based on data-driven insights.
Governance, Risk Management & Compliance (GRC):
- Lead the implementation of GRC models to enhance process improvements and control mechanisms.
- Ensure compliance with internal policies, regulatory requirements, and industry best practices.
Business Collaboration and Leadership:
- Develop credit scorecards for business and risk management purposes.
- Collaborate with business units, risk executives, and other stakeholders to recognize and mitigate key risks.
- Provide recommendations and drive change through effective communication and influence.
Analytical and Quantitative Skills:
- Utilize strong analytical and quantitative skills to back up assumptions with hard data.
- Develop business cases, conduct root cause analysis, and prepare comprehensive credit proposals.
- Maintain high standards of risk and compliance, leading by example.
Periodic Strategy Updates and Testing:
- Regularly update underwriting strategies based on business requirements.
- Evaluate third-party solutions for risk prediction and control.
- Conduct controlled tests to optimize underwriting processes and monitor portfolio metrics.
Presentation and Communication:
- Strong presentation skills to effectively communicate findings and recommendations.
- Ability to translate complex data insights into actionable business strategies.
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DATA ANALYST
A data analyst is essential in an organization for analyzing and interpreting complex data to guide business decisions. They use various tools to extract insights, identify trends, and create visualizations, working closely with stakeholders to develop data-driven solutions for growth.
Role and Skills of a Data Analyst
Technical Skills:
- Expertise in Excel for financial forecasting and reporting.
- Proficiency in Python for data analysis and automation.
- Skilled in SQL and Looker for data manipulation and analysis.
- Experience with Power BI and other visualization tools.
- Knowledge of DAX queries, SAS Base, and Macros.
- Implementing row-level security in Power BI.
Domain Knowledge:
- Experience in finance or mortgage-related fields is beneficial.
Soft Skills:
- Strong organizational skills to manage multiple projects.
- Degree in marketing, finance, statistics, or a related field.
- Experience in startup environments, showcasing adaptability.
Responsibilities:
- Create visual reports, dashboards, and KPI scorecards using Power BI.
- Connect and transform data from various sources for marketing campaign analysis.
- Implement security measures and develop compatible data models for reporting.
Business Intelligence (BI) Analyst
A Business Intelligence (BI) Analyst is essential for turning raw data into actionable insights that guide business decisions. They collaborate with stakeholders to understand data needs, create and deploy reports, and ensure timely delivery of relevant information.
Roles and Responsibilities of a BI Analyst:
- Gather requirements from stakeholders to understand data needs.
- Create and deploy reports and dashboards using BI tools.
- Design and implement data marts and data warehouses.
- Write efficient SQL and other query languages for data manipulation.
- Ensure data integrity and integrate data from various sources.
- Automate regular reporting and troubleshoot BI solutions.
- Use advanced Excel functions and BI tools like Power BI, Tableau, and Qlik.
- Maintain awareness of data privacy and ethical data handling practices.
