Investment Data Analyst, Leading Global Family Office (Middle East based role)

Background:

Our client is a global single-family office with a multi-asset investment strategy and a diversified international portfolio spanning real estate, luxury, energy across both private and public equities. As they broaden their mandate, they are seeking top-tier talent to join their team in the Middle East.

Reporting to the Chief Investment Officer, they are looking for a Data Analyst to be responsible for extracting, organising, processing, cleaning and validating investment-related data from multiple sources. The objective is to present this data in a clear, accurate and exhaustive manner in order to facilitate timely investment decisions.

Roles & Responsibilities:

• Enhancing data collection procedures in their systems to include information that is relevant for building analytic systems and managing their portfolio of investments
• Processing, cleansing, and verifying the integrity of data used for analysis
• Ensuring the availability, quality and security of data used by the Investment Team
• Data mining using state-of-the-art methods and extending company’s data with third party sources of information when needed
• Investigating and interpreting data to identify actionable insights and opportunities for improvement of the performance of our portfolio of investments
• Doing ad-hoc analysis and presenting results in a clear manner
• Presenting findings and recommendations to stakeholders in a clear and concise manner
• Creating automated anomaly detection systems and constant tracking of its performance
• Protecting data to ensure its confidentiality to the most stringent standards and divulging information on a need-to-know basis only

Your profile

• A bachelor’s or master’s degree in data science, computer science, statistics or a related field
• 5+ years of experience, with a minimum of 3 years in the financial industry
• Advanced knowledge of accounting and financial principles
• Proficiency in programming languages such as R or Java
• Good applied statistics skills such as distributions, statistical testing, regression, etc.
• Experience with data manipulation, cleansing, and analysis using tools such as SQL, Excel and other statistical software
• Understanding of Machine learning techniques and algorithms (K-NN, Naïve Bayes, SVM, etc.)