Data Analyst

Data Analytics – Data extraction, cleaning, transformation & summarization 4 Scope of Work

Statistical modelling

Acquisition & behavioral scorecards

Predictive modelling

 

The candidate should have superior problem-solving skills. The ability to understand a business problem & translate into statistical one is a must. 

 He/she should be able to decide on the best modeling/analysis techniques and provide actionable insights/ model algorithms to the business. 

 

 Model development & Deep-dive Strategic Analysis: 

* Data extraction: Extract data from appropriate sources. Require knowledge of databases.

* Data processing: Perform complex data processing (e.g. merging, sorting, data transformations).

* 7 Responsibilities Work with a consultative mindset to gather requirements from other team professionals to identify the right requirements Profiling and analysis: Combine in-depth business knowledge, insights, and techniques such as data visualization, cross-tabs, statistical tests, and data mining techniques to identify risk segments, behavioral trends, product preferences, up selling, cross-selling, and retention opportunities

* Model development: Apply standard and cutting-edge techniques (statistical modeling, data mining, predictive analytics, machine learning, optimization) to identify drivers of a business metric. Compare techniques using validation to arrive at the best model 

Model scoring and business applications of modeling/analytics:

Insert developed models into  the scoring process Improvise the models based on feedbacks from scoring process.

Communication, Coordination & Presentation: 

Work with a consultative mindset to gather requirements from other team professionals to identify the right requirements

Work with tech groups to identify and resolve any data, infrastructure, or implementation issues

Communicate modeling and analysis results and provide professional presentations to sponsors in a simple yet actionable manner.

 

Skills:

Extensive experience of statistical tools e.g. R, Python, VBA, Excel etc.

▪ Experience in statistical modelling using Univariate and Multivariate Regression, Generalized Regression Modelling, Time series forecasting, Non Parametric methods, Logistic Regression, Survival Analysis, Factor Analysis, Clustering, CART, Decision Trees, CHAID, Linear Optimization, Hypothesis testing, Boosting Techniques, Factor Analysis, Scorecard Development etc 

 ▪ Experience in risk scorecards, predictive modelling, machine learning etc. 

 ▪ Ability to extract data from databases, clean and prepare for modelling. 

 Competencies: 

▪ Independently able to deliver results

▪ Able to get to results with only high level inputs

▪ Can solve fairly complex data and modelling problems

▪ Understanding of home loan business

▪ Able to interpret, share and discuss results in the business context

▪ Project Planning, Analytical approach and thinking

▪ Manage and prioritize own work stream