HR at JP Morgan Services
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JPMorgan Chase - Associate - Data Science - Artificial Intelligence/Machine Learning (4-7 yrs)
Data Analytics at J.P. Morgan Corporate Investment Bank combines cutting edge machine learning techniques with the company's unique data assets to optimize all the business decisions we make. In this role, you will be part of our industry-leading data analytics team, and advance the state-of-the-art in financial applications ranging from generating business intelligence to predictive models and automated decision making. The role will be in the firm's Applied AI and Machine Learning organization and will involve working closely with Digital & Platform Services Operations. The successful candidate will apply data analytics techniques from both traditional statistics and machine learning to a combination of third party, publicly available and J.P. Morgan proprietary datasets, with the goal of answering questions relevant to Operations.
- Collaborate with Operations teams to formulate relevant financial and business questions that can be answered by data analysis.
- Research and analyze data sets using a variety of statistical and machine learning techniques
- Communicate final results and give context.
- Document approach and techniques used.
- Work on longer term projects, building tooling that can be used to scale certain types of analyses across multiple datasets and business use cases.
- Collaborate with other JP Morgan machine learning teams.
Required Technical Qualifications and experience :
- 4-7 years of relevant experience.
- MS or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics, Operations Research, Data Science, or similar BS with experience in a highly quantitative position.
- Hands-on experience analyzing data.
- Strong ability to develop and debug in Python or similar professional programming language.
- Problem solving and collaboration skills
Nice to Have :
- Experience with natural language processing (NLP).
- Ideally, some experience with machine learning APIs and computational packages (examples: TensorFlow, Theano, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, statsmodels).
- Experience with big-data technologies such as Hadoop, Spark, SparkML, etc.
- Should be able to work both individually and collaboratively in teams, in order to achieve project goals.
- Must be curious, hardworking and detail-oriented, and motivated by complex analytical problems.
- Must have the ability to design or evaluate intrinsic and extrinsic metrics of your model's performance which are aligned with business goals.
- Must be able to independently research and propose alternatives with some guidance as to problem relevance.
- Must be able to undertake basic and advanced EDA, may require some direction from more senior team; should be aware of limitation and implication of methodology choices.
- Ensures re-use and sharing of ideas within team and locale.
- Able to work with non-specialists in a partnership model, conveys information clearly and creates a sense of trust with stakeholders.
- Shows institutional awareness and some understanding of applied problem solving, may require coaching and guidance as to how to most rapidly reach a satisfactory conclusion