As an Asia Wealth Management Machine Learning Engineer, you'll applying rigorous scientific methods with proficiency in ML Engineering and DevOps capabilities.
You should have a strong knowledge of ML, NLP, Deep Learning, ASR and have experience working with massive amounts of data, and should also have strong software engineering skills and the ability to write maintainable, build fully automated CI / CD pipelines, support software solutions that are customer focused & highly secure.
You’ll be working with and sharing ideas, information and innovation with our global team of technologists from all over the world.
Build and train production grade ML models on large-scale datasets to solve various business use cases for Private Banking
Use data processing frameworks 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.
Ability to 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 modelling experiments, evaluating against strong baselines, and extracting key statistical insights and / or cause and effect relations.
This role requires a wide variety of strengths and capabilities, including :
PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science.
Or an MS with industry or research experience in the field.
Hands-on experience and solid understanding of machine learning and deep learning methods
Solid background in time series analysis, speech recognition or NLP
Experience with machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization)
Experience with big data and scalable model training(applied machine learning, proficient in statistical methods, algorithms)
Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
Very pro-active, delivery focus, strong experience on Agile programming
Excellent analytical, problem solving skills