Quantitative Investment Analyst (Asset Management)

We are looking for a Quantitative Investment Analyst for an International Asset Management Company located in Madrid.

Functions:

• Responsible for the methods and development of tools used for Strategic Asset Allocation that include typical requirements and constraints of an insurer, like economic value generation, risk capital such as SST and Solvency II, liquidity, ESG, accounting and other aspects
• Further develop Asset Liability Management- and SAA-models to optimise group asset allocations and investment capital allocation while considering local legal entity considerations and constraints
• ALM is an important area for Helvetia and QMA. Hence, ALM experience would be welcome. For candidates not proficient in ALM we would expect willingness to familiarise themselves with ALM.
• Develop new investment strategies for Tactical Asset Allocation, build quantitative models to cull investment opportunities. Typical steps include: develop models based on academic research, build prototypes in R / Python, backtesting, simulation, stress-testing, presentation of results, bring the production-ready tool on the Helvetia cloud platform, connect to databases
• Suggest improvements to our liability representation, e.g., to describe mismatches between assets and liabilities and ways to mitigate
• Actively liaise with internal colleagues across the Asset Management and Helvetia organisation to enhance the value generation of our activities and services to our clients

Requirements:

• Master's or PhD in maths, physics, engineering, econometrics, finance or similar with quantitative focus.
• 4 or more years of professional experience
• CFA, FRM, CQF or similar and/or interest in working towards such.
• Interest in digital topics: quantitative investing, programming, visualisation.
• Essential are coding skills in R, Python, VBA and/or other coding languages.
• Experience in optimising / documenting / organising software and/or software governance frameworks.
• Experience / practical knowledge with / interest in applying quantitative models / Machine Learning methods to Finance. Examples are systematic trading strategies, risk models, stress tests, asset allocation, time series analysis, simulation, derivatives.
• Experience with information systems like Bloomberg, FactSet, Refinitiv etc.
• Entrepreneurial spirit, eagerness to learn and to contribute to shape a modern company.
• Very good command of written and verbal English.
• Communication skills, for example, when presenting results to stakeholders and maintaining relationships with colleagues