MoneyOnClick - Data Scientist/Senior Data Scientist

At Werize we are building India's largest financial services brand for the 500 Mn lower middle-income customers in 4000+ small cities (which we call "India 4"). This customer segment has very little penetration of formal loans, insurance or saving products as existing financial service players see this segment as risky and non- profitable. We have created custom lending, insurance and saving products that are tailor-made to serve the needs of India-4 customers.

- To reach these customers at low CAC, we have built a proprietary tech enabled offline- to-online social distribution channel where we have onboarded thousands of financially literate free-lancers such as LIC agents and junior financial sector employees in 1000+ cities. Our fully automated social partner app is available in 6 Indian languages and helps us manage these partners remotely without any physical presence.

- The general perception is that this customer segment is highly risky and non-lendable.

We are happy to share that we have been able to maintain near 0% default rates despite Covid19 . Our proprietary ML based family underwriting where we evaluate the P&L of the family and give a joint liability family loan has helped us maintain these low default rates.

- We have achieved Operational profitability and are on-track to reach EBITDA+ by end of 2021

Fund Raise

Fund Raise: $10.25 million

Stage: Series A (Raised- June 2021- Not Yet announced)

Investors:

Kalaari Capital (Indian VC with $850MN AUM and investments such as Myntra, CureFit, Dream11, ElasticRun)

- Picus Capital (Fund run by Alexander Samwer- Co-Founder of Rocket Internet that had multiple billion Dollar + exits/IPOs - Lazada, Jumia, DeliveryHero)

Our investors valued the founding team's vision, experience, and depth of understanding of this segment resulting in this fund raise

- 3one4 Capital (Early-Stage Fund with $250Mn AUM and companies such as Bank.Open , Licious , Jupiter Neobank in its portfolio)

- Orios Ventures

(Early-stage fund with companies like Ola, Pharmeasy and Go Mechanic in its portfolio) Leadership Team:

Vishal Chopra, Co-Founder & CEO:

- Ex-CBO Lendingkart - India's largest Fintech Lender ($150Mn+ fund raise)

- Fist hire of Amazon India - part of the leadership team that launched Amazon.in

- Regional Director, Souq.com - largest e-commerce player in the Middle East. Raised $425Mn, acquired by Amazon

- MBA, ISB Hyderabad

Himanshu Gupta, Co-Founder & COO:

- Ex-VP and Head of Data Science, Lendingkart - created AI/ML models and maintained

NPAs < 2% vs 8% industry avg

- Data Scientist, IHS Markit

- BTech, IIT Delhi

- 10+ years' experience in AI/ML

Founding Team:

Head of Collections:

South India Head-Collections, Bajaj Finance; managed 800-member team

Head of Product:

BTech, IIT Kanpur; Product Leader at Lendingkart and Flipkart

Head of Technology:

Btech CS, IIT Kanpur; Tech Leader at Paytm and Medlife

Head of Business:

Operations Leader, Lendingkart

Head of Marketing:

Head of Acquisition Marketing, Meesho

Head of Credit Ops:

Regional Head, HDB

Role Description

We are looking for a top talented Data Science head to lead our Data Science team. You will solve some of the most challenging problems in the FinTech industry using Data Science methodologies.

- You will have access to the best facilities, technology, and expertise within the industry and will work on the challenging problems that would include looking at large volumes of data and deriving insights from them. This is an excellent opportunity to be in high-impact teams, where you can truly leverage your skillset and knowledge and bring impact to the business.

- Lead a team of data scientists from top tier schools and collaborate with Founders and business heads to solve complex business problems

- Develop statistical, and machine learning-based models/pipelines/methods to improve business processes and engagements

- Conduct sophisticated data mining analyses of large volumes of data and build data science models, as required, as part of the credit and risk underwriting solutions; customer engagement and retention; new business initiatives; business process improvements

- Translate data mining results into a clear business-focused deliverable for decision- makers

- Working with Application Developers on integrating machine learning algorithms and data mining models into operational systems so it could lead to automation, productivity increase, and time savings

- Provide the technical direction required to resolve complex issues to ensure the on-time delivery of solutions that meet the business team's expectations. May need to develop new methods to apply to situations

- Knowledge of how to leverage statistical models in algorithms is a must

- Experience in multivariate analysis; identifying how several parameters can affect retention/behavior of the customer and identifying actions at different points of the customer lifecycle

- Great business communication skills and have presented to senior leadership

Attributes we are looking for :

- BE/MS/PhD in Computer Science or other technology, Math, Operations Research, Physics, Statistics or Economics (focus on FinTech is a plus);

- Top tier education is a must.

- Demonstrate successful experience in leading a data science team through the implementation of new data science models, tools, and techniques that led to improvement of business performance due to a continued culture of informed decision- making.

- 5+ years of total experience and 2+ Experience in large B2C consumer internet companies is a must

- Extensive experience coding in Python and having mentored teams to learn the same

- Great understanding of the data science landscape and what tools to leverage for different problems

- A great structured thinker that could bring structure to any data science problem quickly

- Ability to visualize data stories and adept in data visualization tools and present insights as cohesive stories to senior leadership

- Excellent capability to organize large data sets collected from many sources (web APIs and internal databases) to get actionable insights

- Initiate data science programs in the team and collaborate across other data science teams to build a knowledge database