Data Scientist Associate Sr

Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity apply your skills and have a direct impact on global business.

You will be building and training production-grade ML models on large-scale datasets, developing end-to-end ML pipelines, and collaborating to develop large-scale data modeling experiments.

Your expertise in Python, PySpark, DL frameworks like TensorFlow, and MLOps will be crucial in this role.

As a Sr Associate Data Scientist you will help build, robust and provide scalable solutions for various banking domains, and also enable the services to be deployed and integrated into existing business workflow.

This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the business processes of a leading global bank.

Job Responsibilities:


* Build and train production grade ML models on large-scale datasets to solve various business use cases for Commercial Banking.


* Use large scale data processing frameworks such as Spark, AWS EMR for feature engineering and be proficient across various data both structured and un-structured.


* Use Deep Learning models like CNN, RNN and NLP (BERT) for solving various business use cases like name entity resolution, forecasting and anomaly detection.


* Build ML models across Public and Private clouds including container-based Kubernetes environments.


* Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems.


* Build both batch and real-time model prediction pipelines with existing application and front-end integrations.


* Collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause and effect relations.

Required qualifications, capabilities and skills:


* Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research).


* Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets.


* Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop.


* Experience in analyzing complex problems and translating it into an analytical approach.


* Experience in Supervised and Unsupervised Machine Learning including Classification, Forecasting, Anomaly Detection, Pattern Detection, Text Mining, using variety of techniques such as Decision trees, Time Series Analysis, Bagging and Boosting algorithms, Neural Networks, Deep Learning.


* Experience with analytical programming languages, tools and libraries (Python ecosystem preferred, but R will be considered).


* Experience in SQL and relational database...