About Data Analytics within Global Research
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 Global Research.
About Global Research
The firm's Research team provides clients with informed views and actionable ideas on economic indicators, markets, companies and asset classes around the world. Its top ranked research analysts, strategists and economists are located in 27 countries in developed and emerging economies. CIB Research strives to be a leader in articulating its unique and independent perspectives in the regions where we want to do business.
Clients of the CIB, Asset Management, and other lines of business value the expertise of Research in helping them achieve their objectives. They recognize its commitment with top-tier rankings in surveys conducted by Institutional Investor, Greenwich Associates, Orion and many others.
- The successful candidate will apply data analytics techniques from both traditional statistics and machine learning to a combination of third party, publically available and JPMorgan proprietary datasets, with the goal of answering questions relevant to our Research analysts and the firm’s clients.
- Collaborate with Research colleagues 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 J.P. Morgan machine learning teams.
Required Technical Qualifications, experience & behaviours
- 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.
- Experience with natural language processing (NLP).
- Strong ability to develop and debug in Python or similar professional programming language.
- Problem solving and collaboration skills
- 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.
Nice to Have
- 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.
- 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
The hiring manager for this job opening would welcome a conversation about flexible working. This could range from ad hoc flexibility in a full time position, to a more formal Flexible Work Arrangement.