JOB DESCRIPTION
As a Vice President, Applied AI/ML Lead in our technology team, you will have the opportunity to solve exciting business problems in the domain of commercial banking, payments, and financial services. You will be expected to have a strong curiosity for data and a proven track record of successfully applying rigorous scientific methods with proficiency in Machine Learning Engineering and DevOps capabilities. This role provides a unique opportunity to apply your skills and have a direct impact on global business.
The ideal candidate will have a strong knowledge of Machine Learning, Natural Language processing (NLP), Deep Learning, Knowledge Graphs and have experience working with massive amounts of data. They should also have strong software engineering skills and the ability to build systems that reach JP Morgan scale.
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:
6+ years working experience as a Data Scientist.
Advanced Degree in field of Computer Science, Data Science or equivalent discipline.
Expertise with Python, PySpark, DL frameworks like TensorFlow and MLOps.
Experience in designing and building highly scalable distributed ML models in production (Scala, applied machine learning, proficient in statistical methods, algorithms).
Experience with analytics (ex: SQL, Presto, Spark, Python, AWS suite).
Experience with machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization).
Preferred Qualifications:
Experience working with end-to-end pipelines using frameworks like KubeFlow, TensorFlow and/or crowd-sourced data labeling a plus.
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