CIB - Data Analytics - Data Scientist - J.P. Morgan - City of London - Wizbii

CIB - Data Analytics - Data Scientist

  • By J.P. Morgan
  • City of London (United Kingdom)
  • Mathematics / Statistics

Job description

Data Scientist

The CIB Data Analytics team at JPMorgan Chase are a small, dedicated team of data scientists, engineers, and commercial specialists focused on creating analysis and developing products that transform the way we and our clients do business.  The team 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 world-class analytics / data science / machine learning team working on problems ranging from natural language processing, through anomaly detection to time series modelling and visualization.    This team’s focus is on the operations, finance, business management and custody areas; so projects are typically targeted here; with a typical project’s duration ranging from 2 weeks to 6 months+.


•             As a hands on practitioner, develop scalable tools leveraging analytics / data science / machine learning and deep learning models to solve real-world problems.
•             Create, curate and protect our data assets; deriving insight through this
•             Collaborate with commercial and analytics team peers, and where needed, across the broader business to deliver value internally and to our clients. 
•             Suggest, collect and synthesize requirements. Create an effective roadmap towards the deployment of a production-level solution.

Key skills and qualifications :

•             MS or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science, or similar BS with 2+ years of experience in a highly quantitative position.

•             Experience in a broad range of machine learning models and data science techniques.

•             Significant experience of hands-on experience developing machine learning models.

•             Ability to develop and debug in Python, Java or R.  Proficient in git version control.

•             Extensive experience with common Python machine learning APIs, computational packages and visualisation tools.

•             Familiarity with basic data table operations (SQL, Hive, etc.),  and should have experience in practical data processing, data mining, text mining and information retrieval tasks.

•             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 commercial 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 effectively capture commercial intent from a broad stakeholder group, and communicate technical concepts and results to both technical and business audiences.


Beneficial Skills


•             Strong background in Mathematics and Statistics.

•             Contribution to open-source projects on Machine Learning.

•             Knowledge in Reinforcement Learning or Meta Learning.

•             Experience with big-data technologies such as Hadoop, Spark, SparkML, etc.


About J.P. Morgan’s Corporate & Investment Bank:

J.P. Morgan’s Corporate & Investment Bank is a global leader across banking, markets and investor services. The world’s most important corporations, governments and institutions entrust us with their business in more than 100 countries. With $18 trillion of assets under custody and $393 billion in deposits, the Corporate & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.  Further information about J.P. Morgan is available at .

JPMorgan Chase & Co. offers an exceptional benefits program and a highly competitive compensation package. JPMorgan Chase & Co. is an Equal Opportunity Employer.

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