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Purpose of role:
As a Data Scientist, you’ll be instrumental in shaping the future of systematic investing at the firm. This is a hybrid research-engineering position that blends rigorous quantitative research with practical implementation. You’ll work on sourcing and evaluating alternative datasets, developing bespoke machine learning models for financial markets, and building scalable, production-grade pipelines that power real-time investment signals. These signals directly inform the firm’s portfolio managers and traders, guiding investment decisions across asset classes.
We are looking for someone who is curious, technically strong, and excited by the challenge of turning complex problems into elegant, data-driven solutions. You should thrive in a collaborative, research-driven environment where innovation, experimentation, and continuous learning are at the core of everything we do.
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Duties and responsibilities:
- Design and implement data science strategies that enhance the investment decision-making process.
- Identify, acquire, and evaluate alternative data sources that may provide predictive insights into financial markets.
- Develop bespoke machine learning algorithms tailored to financial use cases, such as risk forecasting, sentiment analysis, and market regime detection.
- Build, test, and deploy end-to-end data and model pipelines that operate reliably at scale.
- Collaborate closely with portfolio managers, quantitative analysts, and software engineers to ensure alignment between data science outputs and investment goals.
- Contribute to the development of internal libraries, tooling, and infrastructure to streamline data science workflows.
- Stay current with academic and industry research, applying innovative techniques where relevant.
Required experience:
- 1–3 years of experience in a data science or quantitative research role, preferably in finance or another high-impact research domain, or a recently qualified Master’s or PhD graduate.
- Track record of delivering production-ready data science solutions with real-world impact.
- Honours, Masters and/ or PhD level in a quantitative field (Computer Science, Statistics, Engineering, Applied Mathematics, Quantitative Finance or similar).
- Financial certification will be advantageous but not required.
Required Qualifications:
- Strong programming experience in Python, with proficiency in software engineering best practices (modular code, testing, version control, etc.).
- Solid understanding of machine learning, statistical modelling and time series analysis.
- Experience with cloud environments (Azure, AWS, or GCP) and distributed computing frameworks is a plus.
- Familiarity with data infrastructure (e.g., databases, ETL pipelines, containerization, orchestration tools) is highly desirable.
- Use of LLM’s and working with unstructured data is a plus.
- Experience working with unstructured or alternative data (e.g., text, news, satellite, geolocation) is a bonus.
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Key competencies:
- Strong programming experience in Python, with proficiency in software engineering best practices (modular code, testing, version control, etc.).
- Solid understanding of machine learning, statistical modelling and time series analysis.
- Experience with cloud environments (Azure, AWS, or GCP) and distributed computing frameworks is a plus.
- Familiarity with data infrastructure (e.g., databases, ETL pipelines, containerization, orchestration tools) is highly desirable.
- Use of LLM’s and working with unstructured data is a plus.
- Experience working with unstructured or alternative data (e.g., text, news, satellite, geolocation) is a bonus.
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