Job Posted:
07 January 2022
Job Description
work independently through ML lifecycle, from data engineering, model training, model testing to deployment. Proficient in Python
Candidates should have applied ML and other statistical methods for credit scoring, credit risk ( PD, LGD, EAD, Expected Credit Loss), fraud detection, customer segmentation etc.. at Fintechs or banks.
candidates with a very strong interest and aptitude in applying quantitative methodologies and data analytics for solving complex problems in the Investments / Fintech world
Candidates should be helping us develop models to measure and forecast the financial performance and the underlying financial risks in the start ups that we invest in
Alternative lending portfolio, you will also be helping us develop models to segment consumer or SME loans into heterogenous risk buckets and predict probability of default, loss given default and expected credit loss measures
Desired Candidate Profile
5+ Years of relevant work experience
Motivated to work in a Fintech start up.
Curiosity and the eagerness to learn and work collaboratively in a team.
Very strong problem solving skills demonstrated in your academic learning and evaluated through case studies during our interview process.
Sound knowledge in Statistics, Stochastic modelling, Monte Carlo Simulation, Calculus, Linear & Matrix algebra, Machine Learning algorithms.
Basic understanding of financial securities, accounting, venture lending, credit risk. If you have not majored in these topics, It is ok to have self-learnt.
Clear communication skills
Key Skills
Lgd Ead Credit Risk Data Analytics Credit Scoring Data Engineering Customer Segmentation Banking Probability Python