We are looking for a Data Scientist/ML Engineer to join our technology team to solve exciting business problems in the domain of commercial banking, payments and financial services. Candidates must have a strong curiosity for data and a proven track record of successfully applying rigorous scientific methods with proficiency in ML Engineering and DevOps capabilities. This is a unique opportunity to apply your skills and have a direct impact on global business.
The ideal candidate will have a strong knowledge of ML, 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.
What You'll Do:
- 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 domains viz. structured, un-structured etc.
- Use Deep Learning frameworks like CNN, RNN, LSTM and Attention for solving use cases requiring semantic search, named entity resolution, forecasting, anomaly detection among many other techniques
- Build ML models across Public and Private Cloud environments 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
- You will collaborate to develop large-scale data modeling experiments, to evaluate against strong baselines, and extracting key statistical insights and/or cause and effect relations
- Advanced Degree in field of Computer Science, Statistics, Mathematics, Economics, Data Science or equivalent discipline.
- Minimum 1 year of industry working experience as a Data Scientist
- Working proficiency in building Machine Learning Models using Python, PySpark, DL frameworks like TensorFlow/PyTorch on GPU .
- Introductory working knowledge in ML Ops frameworks such as model deployment tools (Rest API, Sagemaker, Containerization, AWS EMR), Database concepts, API concepts, Model Monitoring and management concepts
- Working knowledge with analytical tools (ex: SQL, Hive, Presto, Spark, Python, AWS suite) and databases is a must
- Experience with machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization)
- Exposure to designing and building highly scalable distributed ML models in production
- Experience working with end-to-end pipelines using frameworks like Sagemaker, crowd-sourced data labeling tools are plus points
- Experience working with AWS is a plus
JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.