Golden Gate Ventures


Data Scientist at Lenddo
Mumbai, IN
About Us:

At LenddoEFL, our vision is to provide life-changing financial products to more than one billion underserved individuals around the world. We believe that financial inclusion isn't simply about access to financial products, but about access to fast, affordable, and convenient financial products. And we believe in empowering people to use their their digital profiles and personality traits to increase their financial options.

We offer leading alternative credit scoring, identity verification and insights products to financial institutions in more than 20 emerging markets. To date, LenddoEFL has allowed more than 50 financial institutions to run over 6 million assessments for people with limited information, allowing them to disburse over $2 billion USD in credit. We are already changing the world, but we are just getting started. Will you join us for our next chapter of growth?

About the role:

The Data Scientist will work with research scientists/engineers to develop, prototype and productize new machine learning and graph algorithms operating on big data. He or she will design scalable big data pipeline/workflows for behavioral and social algorithms.


Review and analyze client and external data sets.

Deploy and integrate EFL/Lenddo's solutions in clients’ environments.

Establish and implement the risk frameworks including design variables and scorecards, model validation and portfolio performance monitoring.

Quantitative credit risk activities including modeling, validation, dual risk rating, loss and migration.

Research new non-traditional sources of data to include in the Lenddo platform.

Educate customers risk teams on the EFL/Lenddo solutions and methodologies (including coordination of Workshops, Webinars and White papers development).

Assist the commercial team during the sales process.


Advanced degrees in Applied Math, Statistics, Engineering or equivalent

5+ years of job experience with statistical modeling with a focus on unstructured data.

Strong programming skills.

Required: Experience with statistical techniques and tools in python or R.

Preferred: Experience in a wide range of numerical and statistical modeling, including pattern recognition, machine learning and NLP.

Good communication skills; prefer candidates who have experience of presenting technical concepts and results to risk and business teams.