The Investment Data Science team is part of the Generali Asset Management Investment Function. The team develops and coordinates projects and initiatives that leverage data science techniques to enhance investment strategies.
As an IT Developer within the Front Office, the candidate will work, within the Investment Data Science team, on refactoring and potentially improving the code of a quantitative investment strategy currently applied to tactical asset allocation and equity sector allocation.
The current implementation could theoretically be deployed on numerous mark‑to‑market portfolios, but its complexity, along with high computational and memory demands, limits this potential. The primary task over the next six months is to understand and enhance the code, making it more versatile, faster, and intuitive.
Structurally reorganize the code to ensure that the two main workflows (monthly outputs and annual reviews) are testable and free from obsolete implementations
Automate the generation of output slides using updated market data
Automate the storage and backup of data and historical results
Conduct testing campaigns of the methodology on various multi‑asset portfolios
Requirements
Our ideal candidate will meet the following requirements:
Bachelor’s Degree (1st level, 3 years) in Computer Engineering, Computer Science, Mathematics, Physics or a related field
Proficiency in Python, with proven capabilities in back‑end development
Genuine passion for scientific coding and a keen interest in quantitative finance
Previous hands‑on experience with financial topics is a plus
Skills
Strong interpersonal skills and the ability to work effectively in an international team/environment
Proven ability to communicate complex analytical concepts to a general audience
Flexibility and ability to manage changing deadlines
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As an IT Developer within the Front Office, the candidate will work, within the Investment Data Science team, on refactoring and potentially improving the code of a quantitative investment strategy currently applied to tactical asset allocation and equity sector allocation.
The current implementation could theoretically be deployed on numerous mark‑to‑market portfolios, but its complexity, along with high computational and memory demands, limits this potential. The primary task over the next six months is to understand and enhance the code, making it more versatile, faster, and intuitive.
Structurally reorganize the code to ensure that the two main workflows (monthly outputs and annual reviews) are testable and free from obsolete implementations
Automate the generation of output slides using updated market data
Automate the storage and backup of data and historical results
Conduct testing campaigns of the methodology on various multi‑asset portfolios
Requirements
Our ideal candidate will meet the following requirements:
Bachelor’s Degree (1st level, 3 years) in Computer Engineering, Computer Science, Mathematics, Physics or a related field
Proficiency in Python, with proven capabilities in back‑end development
Genuine passion for scientific coding and a keen interest in quantitative finance
Previous hands‑on experience with financial topics is a plus
Skills
Strong interpersonal skills and the ability to work effectively in an international team/environment
Proven ability to communicate complex analytical concepts to a general audience
Flexibility and ability to manage changing deadlines
#J-18808-Ljbffr
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