WeRize - Data Scientist (2-10 yrs)

About WeRize

Founded in 2019 by Vishal Chopra and Himanshu Gupta, WeRize is building India's largest full stack financial services brand for 300 million underserved middle-class customers who live in 4000+ small towns of India. 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, credit cards, health and life insurance and investment products. WeRize manufactures innovative Credit, Savings and Group 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 2000+ 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 1000+ 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.

To know more about the company, please visit: https://www.werize.com/

About the team :

The Data Science team at WeRize plays a critical role in all the decisions, both small and big. This is a strategic team that helps solve business problems across all parts of the organization to drive growth, manage risk, improve operations, etc. Some examples of the problem statements that this team addresses:

- Should credit be offered to a segment of customers? What should the interest rate, tenure, and sanction amount be?

- How to improve collections efficiency?

- How to reduce the cost of customer service operations?

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.

What are we looking for?

- Demonstrable Modelling experience: In depth understanding of model build and model lifecycle management across supervised and unsupervised learning using structured and unstructured data. (eg: Risk Scorecard, Propensity models, Optimization, NLP, etc).

- Excellent coding skills: Python coding (a must skill) and developing SQL queries.

- Strategic & Analytical Orientation: Exceptional Logical, Analytical, Reasoning skills complemented by rigor and strong quantitative orientation.

- Strong communication skills: Impeccable written and oral communications with ability to engage directly with stakeholders.

- Self-driven: Motivated and comfortable with ambiguity.

- Guide & Train: Ability to guide and train juniors.

Preferred Qualifications & Experience

- Level of experience: 2 to 5 years

- Graduate or Postgraduate in Engineering, Math, Statistics, Economics, Operations Research

- Should have demonstrable financial domain specific working knowledge of model development.