Develop and deploy AIML (Artificial Intelligence and Machine Learning) models for specific business applications. Apply advanced techniques in Machine Learning, statistics, and applied mathematics using Python and optimize and scale models for performance, accuracy, and efficiency
Work with large datasets, perform data preprocessing, AI feature generation, and model evaluation
Spot opportunities where AIML can support and prioritize the implementation for the team accordingly
Knowledge in GenAI and able to support GenAI roll out use case together with regional GenAI team focus on prompt engineering and model monitoring
Increase the transparency of the AIML / GenAI capabilities and promote AIML culture across different business units
Technical And Behavioral Skill Requirements
Master's or bachelor's Degree or above in computer science, mathematics, statistics, business analytics, physics, or related disciplines
A proven record of a strong foundation in Machine Learning, data science, and software engineering. Ideally with 8+ years of data science/analysis project experience in Machine Learning and AI data modeling and embed critical thinking and ability to communicate complex analysis/models across a diverse team
Champions a collaborative mindset and a drive to learn, iterate, and improve
Plus - Ability to explain results using Tableau or other visualization tool
Plus experienced in Banking and Financial industry
Excellent software skills (Python, SQL, Pyspark) and knowledge in design patterns and code optimization. Familiarity in libraries such as Pandas, TensorFlow, XGBoost etc is also critical
Strong Mathematics and statistics skills with experiences with ability to do model evaluation, feature importance and Bias/ Variance tradeoff
Machine Learning knowledge on knowing how models work and when to use them include core ML Algorithms (supervised and unsupervised and Advanced ML include Deep Learning, NLP and recommendation systems.)
Model evaluation and experimentation skills and experience as to validate models using cross validation, A/B testing, ROC/ AUC etc
