National Manager - Analytics - Financial Services Firm

Analytics :

- For a company with customer base of more than TENS OF MILLIONS

- Lending of more than 1 HUNDRED THOUSAND CRORES

- Statistics OR Engineering Background is preferred.

Statistical Modelling :

- Creating and building internal scoring models (such as application scorecard, behavior scorecards, building a Persona-based output model etc.) using statistical regression and machine learning techniques to determine the risk of customers based on credit performance within and outside BFL.

- Ideate and identify opportunities to leverage analytics to achieve business priorities (like reducing the Flow rates, improving efficiency by choosing the right set of customers and defining treatment accordingly).

- Review the business requirement documents (BRD), facilitate the creation of relevant models, review testing of models in the system and provide sign off for implementation of models.

- Review and provide direction at every step of modeling including the choice of algorithms, creation of features techniques used for the development of algorithms, results of the algorithms, the application of models and approve each toll gate for modeling.

- Seek feedback, validation, buy in and sign off from the Risk Officers of the various businesses and Business heads for the developed models

Enhancing Collection Efficiencies :

- Creating and building models for increasing collection efficiencies through various treatments.

- Analysis and segment Performing and Non-performing portfolio and define treatments basis customer persona of segment

- Building and strengthening the Channel affinity model for choosing and targeting right set of customers.

Functional review :

- Analyse and monitor the performance and results of all models on a monthly basis, and report to CCO and Collection head.

- Periodically evaluate the models for business usability and monitor deterioration to take the decision on rebuilding or tweaking of the models to cater to the changes in requirements

- Review business performance, health indicators of the portfolio to evaluate the requirements for change or adjustment or redevelopment of models.

Market Awareness :

- Keeping abreast with new analytical tools and techniques in the external market

- Enhance the analytical capability of the organization through trainings and usage of the new tools and techniques available in the market.

- Minimum 15+ years of experience in data analytics, preferably in financial industry

- Experience of Analytics and machine learning models using open source tools like R/Python.

- Should have dealt with large amount of data in past & understand intricacies of impact data can have

- Adept in building models using bagging, boosting techniques, neural networks, KNN, SVM, Na- ve Bayes, Stochastic methods, ANOVA etc.

- Adept in using data visualization libraries like Matplotlib, Seaborn, ggplot, R-Shiny etc.

- Experience with SQL & Cloud based data infrastructure to identify patterns, trends and outliers in data.

- Must have experience in both unstructured data (text analytics, voice analytics) and structured datasets to bring out ML based solutions.

Machine Learning :

- Build machine learning models to prioritize collections with multiple payment channels, multiple workflows & diverse product segments to improve collections efficiency and increase collections.

- Transform processes using daily ML model execution for collection campaign execution.

- Develop Well-Defined ML strategies for Collections of Delinquent Loans - Customer Segmentation, Prioritization and Effective channel strategy, Forecasting, Simulations, Building strategies to improve business performance.

- Work on data models, variable transformation and identify-capture-use new data sources to build machine learning models.

- Work on loss forecasting models to precisely forecast loss numbers

- Creating Strategy for sharper Communication strategy for Delinquent customers.

- Scan the environment and be up to date on emerging trends globally in machine learning and build use cases.

- Analyze current environment & collection trends and provide improvement solutions.

- Architect ML solutions in cloud infrastructure and optimize business processes wherever required.

- Effective communication with stakeholders - teams, Business partners, IT partners and Senior Management.

Team development :

- Participate in the recruitment process to identify the right talent at senior positions within the function

- Establish individual performance expectations and regularly review individual performance of the team

- Identify the development needs of teams and provide appropriate opportunities (training for professional development etc.) to ensure teams are motivated and equipped to drive business

- Project management capabilities to drive projects through multiple priorities

DECISIONS :

- Decide which data sets should get used and which ones should not be used; this often involves a cost-benefit analysis.

- Any new variables to be introduced and maintained for improving the efficiency of the analytics model.

- Choice of external partners for data sourcing and analytics

- Decide which data sets should get used and which ones should not be used; this often involves a cost-benefit analysis.

- Any new variables to be introduced and maintained for improving the efficiency of the analytics model.

Stakeholder Management: (Internal as well as external):

Internal :

- Collection Team: To understand and align on business targets and ensure usability of data and analytics models

- BI and Communication Team: To streamline the Data and create campaign to communicate with customers through SMS and BOT campaigns.

- Risk Team: To align the business metrics with RISK team

- IT team: To align with the available data and frequency of same

- Wallet Team: For aligning on SDK data and using the same for making model sharper.

External :

- Strategic Analytics Partner for developing Analytical and Statistical models.