Lenddo Jakarta, Indonesia
Feb 07, 2019Full time
About the Position: LenddoEFL is seeking an experienced Data Scientist to execute and improve our modeling efforts with an innovative, challenging alternative to traditional credit scoring. As a Data Scientist on LenddoEFL’s modeling team, you will be responsible for building, monitoring and deploying different types of models. The team works on non-traditional credit scoring techniques and is continuously looking for improvement on its standards. The role: Work with research scientists/engineers to develop, prototype and productize new machine learning and graph algorithms operating on wide and big data. Design scalable big data pipeline/workflows for behavioral and social algorithms. Build strong communication channels with different stakeholders Specific responsibilities Review and analyze client and external data sets. Deploy and integrate LenddoEFL's solutions in clients’ environments. Establish and implement the risk frameworks including design variables and scorecards, model validation and portfolio performance monitoring. Design, build and implement Data Manipulation environments for internal and external purposes Rapid identification of R&D opportunities and model improvement. Research new non-traditional sources of data to include in the LenddoEFL platform. Preferred Qualifications: Advanced degrees in Applied Math, Statistics, Engineering or equivalent 5+ years of job experience with statistical modeling Strong programming skills. Experience with statistical techniques and tools in python or R. Good communication skills; prefer candidates who have experience of presenting technical concepts and results to risk and business teams. Plays well with others; able to listen to, appreciate, and effectively critique peers’ opinions. Preferred Qualifications: Proven experience in Data manipulation. Database skills in any major database language. Deep fundamental understanding in the technical aspects, monitoring and evaluation techniques, and the challenges associated with traditional credit scoring. Demonstrated interest in machine learning and other non-traditional techniques for model construction, and able to leverage and work with skills of highly experienced data scientists. Experience in a wide range of numerical and statistical modeling, including pattern recognition, machine learning, and NLP. Experience (or keen desire for experience) in a small, fast-moving, high-growth company. Any level of experience with Spanish.