Data Scientist at Prescient

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.

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.

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.