Job Description

- The finance industry has ignored billions of deserving individuals. We are a mission driven team of finance, analytics and technology professionals, focused on driving financial inclusion to serve the underserved across their credit, savings and financial needs.

- This role will be accountable for end to end ownership(build, deploy, monitor, enhance) across the modeling lifecycle using various machine learning/AI based scoring models to assess risk, uptake, churn, collections and usage for digital consumer & merchant lending businesses.

What we look for:
- Passion for analytics: Should have solid understanding of cutting edge credit strategy leveraging data driven models.

- Should have hands on experience and track record of delivering projects in individual capacity. -

- Process oriented: Should help in building a process that maximizes operating efficiency while maintaining risk across multiple lending cycles.

- There needs to be an obsession with collecting and analyzing data to drive business iterations and improvements.

Stakeholder Management: Should have a track record of working with partners across functions internally as well as externally to build effective relationships and drive business deliverables. -

Willingness to go above and beyond: For start-ups the responsibilities and needs of the business change quickly.

- We\'re looking for someone who is not afraid to take on calculated risks and can deal with ambiguity.

Job Responsibilities:

- Model Management Lifecycle Development Data collection from multiple partners (Telco, Wallet, CRB, FI, Govt. database) Creation of various scoring models leveraging big data platform

- Model Deployment Deploying models in cloud and in-premise set up leveraging Setting up process for periodic scoring Model monitoring

- Develop and deploy CSI/PSI process Periodic model refresh/recalibration process Partner Management & Governance

- Managing engaging with partner data / BI team for collecting and understanding data

- Review meeting with partner credit / collection /fraud team Collaborate with internal credit policy/portfolio mgmt. team to drive P&L outcomes MIS Process Management

- Building reports for model monitoring and drive enhancements

- Arranging training sessions to up skill the team in analytics problem solving and machine learning algorithms

Required Qualifications & Skills:

- Strong concepts in statistical model building Previous experience (more than 2 years) in building models in Bank/ Fin-tech/Telco Hands on experience in building credit risk models (PD/LGD) using R, Python, Pyspark etc

- Previous experience in working with SQL on a big data stack (Hadoop/Spark/ML Flow) Strong entrepreneurial drive

Good to have: Experience in working with credit card firm Hands on experience in working with Telecom data

About Yabx :

- Yabx has offices in Amsterdam, Gurgaon, Bangalore, Bogota and Nairobi. The founding team consists of people from IIMs, IITs and Ivey League Business Schools. The team has significant data analytics, financial services and global entrepreneurial experience.

- They are a bunch of supercharged individuals passionate to deliver financial access to each and every individual. The company was incorporated by Mahindra Comviva in 2017. The Yabx platform connects banks and financial services providers with prospective customers.

- These are small & medium enterprises and individuals who need real-time access to credit. For the same, Yabx works with partners such as mobile network operators and mobile wallet providers to reach this target segment.

- Yabx deploys advanced models using artificial intelligence, machine learning and data analytics to process massive volume of data from telecoms and mfs providers and further analyzes this data with the help of these algorithms to derive useful insights. Yabx\'s mission is to create world-class innovative products to enable banks and mfis to further improve the lives of these underserved people.

More details available on https://yabx.co Yabx Technologies (Netherlands) B.V.

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