The Team 

The Data and Machine Learning division at Man Group is dedicated to ensuring the business can generate valuable insights from data. The team owns the sourcing and delivery of traditional and alternative data to our investment teams as well developing and supporting Man Group's central data platform. The team is also responsible for development of generative AI tooling to drive innovation and accelerate business processes. 

The function seeks to unlock the value in data by partnering with investment teams to source new and diversifying datasets and build scalable evaluation methods and insights on data. The core value which unifies us is a passion to utilise science, technology, and data to enhance our investment and business processes. 

The Role  

As an Analyst in the Data Science Analysts team, you will use your specific and general skills to support the quantitative research and portfolio management teams in the development of data driven trading models. Your focus will be on acquiring, cleaning, mapping, and analysing, large structured and unstructured datasets for alpha generation. On some projects you will act as a subject matter expert, delivering high quality exploratory data analysis and insights.  

You will have responsibilities ranging from data vendor scoping through to data ingestion, exploratory analysis and prototyping robust data pipelines. The team’s aim is to provide a consistent and scalable approach to data delivery and analysis along with a low touch data management process. This is delivered through a series of small self-managed projects working with the relevant investment teams and other members of the data team. 

Responsibilities  

• Collaborate with data sourcing and commercial management, compliance, data engineering, research, portfolio management, operations, and technology to understand investment needs and manage the on-boarding process of new diverse datasets for Man. 

• Perform thorough data vendor comparison analysis covering data integrity, coverage and aggregation methodologies, while simultaneously building a strong knowledge of the alternative data landscape. 

• Develop large-scale data analytics capabilities to extract insights from structured and unstructured data. 

• Build automated processes for reviewing and analysing diverse sets of information. 

• Support systematic and discretionary investment teams by cleaning, mapping, interpreting and performing ETL (extract, transform, and load), on large and messy alternative, traditional and web-scraped datasets.  

• Curate and present relevant data research and technical documentation to build and maintain an updated, accurate and accessible knowledge store. 

• Ensure that vendors are actively engaged with Man’s requirements and provide a continuous feedback loop between Man’s investment units and vendors. 

• Contribute to initiatives across the Data and Machine Learning group to expand and improve the firm’s data ecosystem. 

• Conduct industry research to identify new initiatives in financial markets, market data and alternative data. 

Requirements  

• Strong academic record and higher education degree with high mathematical and computing content e.g. computer science, mathematics, physics, statistics or another discipline involving technical and quantitative analysis techniques. 

• Proven experience and fluency with data analysis techniques in an object-oriented language, preferably Python, along with relevant libraries e.g. NumPy/Pandas. SQL experience is a plus. 

• Experience creating technical solutions for the operation and visualisation of data related tasks and processes.  

• Experience with mapping and analysing large alternative datasets is preferable but not essential. 

• Working knowledge of Snowflake, Linux / UNIX, Git, Jira is preferable.  

• Demonstrate evidence of strong analytical and communication skills, both written and oral. 

• Financial industry experience is preferable but not essential.  

• Excellent attention to detail. 

• Self-organised with the ability to effectively manage time across multiple projects and with competing business demands and priorities.