Well-known and respected financial services firm has a challenging opportunity available for an experienced Financial Engineer / Data Scientist / Quant with a passion for the financial markets and for analysing large data sets, and who enjoys building highly complex models, programs (including machine learning) and tools to identify trends that most others would miss.
The objective of this role is to identify trends in trading data across all financial instruments that could indicate attempts of price manipulation and, or unregulated suspicious trading activities.
Given the firm’s positioning and profile in the SA financial markets, this opportunity provides the successful incumbent with unparalleled exposure to essentially all listed financial instruments across all asset classes and markets, as well as obtaining a big-picture view of the mechanics of the SA financial system.
The successful incumbent will be responsible for:
• Market Data Collection: Gathering data from financial markets, including prices, trading volumes, and economic indicators, and ensuring its accuracy and quality.
• Financial Data Analysis: Using all possible avenues i.e. systems, tools, programs and methods to analyse data for trends and trading performance to uncover patterns and insights.
• Visualization: Creating charts, graphs, and dashboards to present financial data and market trends to stakeholders.
• Reporting: Communicating insights and recommendations to internal and external role players and other stakeholders through detailed reports and presentations.
Requirements:
• Numerically orientated post-grad qualification (BSc / BBusSci / Actuarial / Quantitative Management / Physics / Computer Science).
• CQF, FRM, FASSA (or studying towards) a distinct advantage.
• A minimum of 5 years’ financial markets experience in Data Science / Quantitative Analysis / Market Risk / Market Surveillance / Product Control.
• Excellent understanding of the pricing and valuation of financial instruments and derivatives across all asset classes.
• Statistical Knowledge: Understanding of statistical methods and tools relevant to financial data analysis.
• Programming: Proficiency in languages like Python, R, or SQL, with a focus on financial libraries and packages.
• Data Visualization: Skills in tools like Tableau, Power BI, or financial-specific tools like Bloomberg Terminal.
• Machine Learning: Knowledge of machine learning algorithms to develop predictive models for market analysis.
• Financial Acumen: Understanding of financial markets, instruments, and economic principles to contextualize data analysis.
• Passion and love for financial markets.
• Accuracy and superb attention to detail.
Note: A candidate’s CV would under no circumstances be discussed / disclosed without the candidate’s explicit, prior consent.
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