Founded in 2019 by Vishal Chopra and Himanshu Gupta, WeRize is building India's largest full stack fintech platform for 500 million underserved middle-class customers who live in 5000+ small towns of India.

WeRize (Wortgage technologies pvt ltd) also owns RBI registered NBFC subsidiary (Wortgage Finance pvt ltd).

This customer segment is not served by private sector banks, Insurers and Mutual Fund companies due to their low ticket-size and lifetime value and is dependent on PSU/Govt. banks.

PSU/Government banks rarely provide financial products beyond basic savings accounts and these customers lack access to unsecured loans, MSME loans, credit cards, affordable housing loans, loan against property , health and life insurance and investment products.

WeRize manufactures innovative unsecured consumer credit, mortgages, loan against property, MSME loans, savings and insurance products designed for this customer base keeping in mind their needs, requirements and purchasing power, with a view to add a layer of financial security to their lives and enable access to credit.

While customers in these geographies use smartphones, they need proper guidance and support when purchasing the right financial products for themselves.

So, a pure digital model doesn't work for this segment.

WeRize has innovated on this front through its 'Finance ki online dukaan (Social Shopify of Finance)', a first of its kind social distribution tech platform in the financial services space that educates and enables local financially literate freelancers across these small towns to source business through online and offline channels, recommend the right financial product(s) to customers as well as provide after sales support.

These freelancers, who are located in more than 5000+ towns and cities, earn as much as INR 30,000 a month from WeRize in commissions.

Our social distribution platform supported by financially literate freelancers means exceptionally low cost of customer acquisition (CAC) and operations costs compared to both fully digital and on-the-ground financial services providers.

Digital conversions among this target group are way lower when compared to upper income customers in metros and hence pure digital CAC doesn't work for this segment.

While companies like LIC and Fino Bank also rely on freelancer distribution, they deploy local on-field teams/branches to manage freelancers in every city.

That results in very high CAC and operations costs for such companies.

WeRize on the other hand, has been able to acquire, train and manage thousands of freelancers in 5000+ cities only through its tech platform and without any feet-on-street team of its own.

This results in highly profitable business model for Werize.

About the role:

- You will help solve problems at WeRize across credit/fraud risk, business, collections, customer service, etc through AI/ML techniques.

- As a key member of the team, you work closely with leadership and business/functional units to manage model lifecycle of build, validate, implement, monitor, and update.

Responsibilities:

- Solve business problems across all functions using AI/ML.

- Ownership of model lifecycle management.

- Guide and train junior team members.

Key Responsibilities:

- Build AI/ML solutions to solve business problems across credit risk, fraud, collections, customer service, and other business functions.

- Own the complete machine learning model lifecycle-from problem definition and model development to deployment, monitoring, and optimization.

- Develop predictive and prescriptive models using structured and unstructured data.

- Collaborate with business stakeholders to translate business challenges into data-driven solutions.

- Mentor and guide junior data scientists and analysts.

- Present analytical insights and recommendations to technical and non-technical stakeholders.

Required Skills:

- 4-8 years of hands-on experience in Data Science, Machine Learning, or Applied Analytics.

- Strong proficiency in Python (mandatory) and SQL.

- Experience building and managing ML models across supervised and unsupervised learning using structured and unstructured data.

- Hands-on experience with one or more of the following: Credit Risk Scorecards, Fraud Detection, Propensity Models, Optimization, NLP, Classification, Regression, Clustering, and Model Lifecycle Management.

- Strong analytical, quantitative, and problem-solving skills with the ability to translate business problems into scalable AI/ML solutions.

- Excellent written and verbal communication skills with experience working directly with business stakeholders.

- Self-driven, comfortable working in ambiguous environments, and capable of mentoring junior team members.

- Demonstrable experience in financial services, fintech, banking, or lending with exposure to financial domain-specific model development