Open Position

Job Description: The ISI Foundation is seeking to appoint a Senior Data Scientist and a Data Scientist to undertake applied research activities within the recently-funded Innovation Laboratory on Artificial Intelligence of Intesa SanPaolo, a mixed initiative between fundamental research performed at ISI Foundation and real-world business-driven challenges posed by one of the major Italian banks.

The project aims at leveraging the fully anonymized temporally-resolved data of firm-to-firm transactions to investigate cascading effects on credit risk in a real setting. The goal is to devise an actionable pipeline able to extract meaningful features based on the relationships between companies’ risk levels, their economic-financial performance and their role within the supply chain to outline novel predictive models (focusing on "time to detect" and "advanced early warning"). The ambition is to define a new methodology for estimating credit risk that will enabe the development of a new generation of products favoring the innovation of the Bank-Company relationship.
The project will be run in direct contact with professionals of the financial sector (risk and lending specialist), and a scientific advising will be provided by a multidisciplinary faculty team including Fondazione ISI’s Senior Scientists.

Initial appointment will be for a period of 1 year, with the possibility of renewal.

According to your experience, your responsibilities will involve: Carrying out innovative and business-driven research. Delving into data from different systems, at different timescales, to discover hidden relationships and valuable information. Developing and implementing data science pipeline aimed at bringing profitable and actionable insights into real-world applications. Producing high quality scientific and technical outputs that might be submitted as journal articles, conference papers, and presentations.

As a Data Scientist you will:
  • Acquire advanced financial lexicon by working in contact with Credit Risk, Innovation and Data Office practitioners Familiarize with internal/external databases
  • Familiarize with credit risk engine and early warning system used in bank.
  • Identify economic-financial variables that best define company riskiness
  • Analyze topology of network based on interconnections between companies
  • Estimate potential contagion effects between companies and potential areas of risk concentration within the network
  •  Design and implement a risk model that takes into account the features of the individual companies and their role in the networked ecosystem;
  • Perform a sensitivity analysis on the estimated risk under system perturbations (temporal dynamics of the network structure, missing data, etc..)
  • Draft a technical report to describe and outline performances, advances and limitations of the "early warning" model developed.
Core skills you will need: Ph.D./Master in Computer Science, Physics, Mathematics, Software Engineer or equivalent experience, with special focus on one of the following fields: Network Analysis and Graph Analytics, Data manipulation and data analysis, Credit Risk Modeling, Applied statistics, Machine Learning, Econometrics. Strong programming skills in Python/R or C/C++ or Java or SAS. Creativity and excellent personal skills. Familiarity with financial mathematics will be a plus. Familiarity with GPU programming will be a plus.

As a Senior Data Scientist you will:
  • Manage and lead the research of the Data Science Team
  • Acquire advanced financial lexicon by working in contact with Credit Risk, Innovation and Data Office practitioners Familiarize with internal/external databases
  • Familiarize with credit risk engine and early warning system used in bank.
  • Define and design the computational experiments to implement the model.
  • Identify economic-financial variables that best define company riskiness.
  • Analyze topology of network based on interconnections between companies.
  • Estimate potential contagion effects between companies and potential areas of risk concentration within the network.
  •  Design and implement a risk model that takes into account the features of the individual companies and their role in the networked ecosystem;
  • Devise the proper sensitivity analysis to assess the robustness of the estimated risk under system perturbations (temporal dynamics of the network structure, missing data, etc..)
  • Consolidate a technical report to describe and outline performances, advances and limitations of the "early warning" model developed.
Core skills you will need: Ph.D. in Computer Science, Physics, Mathematics, Software Engineer and >3 years of experience in developing/managing data-intensive R&D projects, with special focus on one of the following fields: Network Analysis and Graph Analytics, Data manipulation and data analysis, Credit Risk Modeling, Applied statistics, Machine Learning, Econometrics. Good programming skills in Python/R or C/C++ or Java or SAS. Creativity and excellent personal skills and demonstrated ability in leading small research groups. Familiarity with financial mathematics will be a plus. Familiarity with GPU programming will be a plus.


About the Lab: The research is organized in small and agile research groups addressing vertical business challenges under the scientific advisory of the ISI Foundation. We provide a truly international and multidisciplinary research environment with plenty of opportunities for collegial interaction in a highly competitive setting, where unique data-assets from one of the major Italian banks are at the core of data-intensive research to address challenging business-driven problems. We provide a competitive salary according to the qualifications and medical and pension benefits according to the Italian State.

 The ISI Foundation is an equal opportunity employer and does not discriminate on the basis of ethnicity, gender, sexual orientation, age, religion or disability.

To apply, please send your cover letter, curriculum vitae and professional reference list to <applied-research@isi.it>