Lead - Data Analyst/Data Science - Collections Segment - Bank

Given below position details with a Leading Bank :

Role/Job Title: Lead Data Analyst / Data Science - Collections

Business: Data Analytics

Function/Department:Data & Analytics

Place of Work:Mumbai BKC

Qualification from a premier institute is a must.

Job Purpose :

'The Lead Data Analyst role:

- Identifies business needs and problems by constantly liaising with collections team

- builds analytical solutions to solve for problems at any of the collections life-stages like: a) Pre-delinquency management b) Self cure c) Early and late buckets allocation d) Settlement and Recovery propensity models

- Develop challenger programs to existing champions and scale up basis measurable outcomes

- Constantly search for new sources of Data which can be used for improving collection efficiency

- Mentor junior resources in the team to parallelize work streams through effective delegation

Job & Responsibilities:

- Identify and scope business requirements and priorities by liaising with collections stakeholders to effectively explore available options to solve the problem

- Use current and new sources of data to build data backed solutions in a structured or unstructured manner. Monitor and maintain such solutions on a periodic basis

- Design implementation plans for above solutions in line with existing or planned tech stacks of the organization to deliver optimal outcome in most efficient manner

- Develop a test strategy and systematic test procedures to verify and ensure that solution meets its design specifications as well as the performance, load and volume levels set out.

- Manage stakeholder expectations and needs by aligning those with requirements and objectives of the organisation.

- Identify and evaluate digitisation and innovative business opportunities provided by new advancements in data & analytics to establish new services or businesses to bridge the physical and digital worlds

- Develop, select and apply analytical tools and advanced computational methods to enable systems to learn, improve, adapt and produce desired outcomes or tasks

Key Success Metrics:

- Development of analytical tools and their deployment resulting in efficiency improvement in terms of : a) roll forward improvement and b) cost optimization

- Conduct frequent tests to challenge status quo and measure performance

-Manage a team of analysts through effective delegation