Job Description
We are seeking a highly skilled and experienced Data Scientist who will build and own machine learning models with the end goal of mitigating risk across merchant and user journey while enhancing growth across different segments of our business.
You will work on developing classical machine learning as well deep learning models for Risk (fraud, credit and operational risk) use cases as well as building in-house foundation models for learning representation of entities like customers, merchants, devices etc. in our ecosystem. In this role you will work with a talented engineering and data science team to build and deploy models at scale. You will have the opportunity to apply and improvise over the latest breakthroughs in AI research from industry and academia and bring them into life with our data product offerings.
What does this include?
Design, build, evaluate and maintain ML/DL models to detect various types of risk mitigation problems while improving end user experience
Collaborate with product and analytics partners for business to ML problem formulation and align on key deliverables
End-to-end management of the entire lifecycle of the machine learning or deep learning project, from identifying objectives and preparing data to testing and validating models, calibrating them, monitoring and feedback loop.
Unsupervised learning methods to augment existing supervised models, or detect portfolio anomalies
Requirements:
Have developed and deployed ML models at scale
Real time feature engineering pipeline and model development is a plus
Knowledge of classical ML models for classification and regression. Should have developed and deployed one such model from scratch. Know the trade off between choosing the techniques
Working knowledge on Sequential Neural Network and Knowledge Graph representation learning using Graph Neural Network is a plus
Excellent understanding of ML/DL frameworks (Keras/Tensorflow/PyTorch etc.) and libraries (scikit-learn, etc.).
Excellent understanding of computer science fundamentals, data structures, and algorithms. Should have familiarity with object-oriented design methodology and application development in Python.
Familiar with Big Data related technologies to manage large volumes of complex data (SQL, pyspark). Knowledge of Scala is a plus
Passion to learn and build quick POC’s with state-of-the-art ML/DL algorithms.
Challenging the norm, creative thinking, collaboration
Working experience of fraud risk is plus
BS in Computer Science or MS in Data Science discipline (or equivalent).
