Systematic Trading Data Analyst

  • Cargill
  • Geneva, Geneve, Switzerland
  • 23/05/2022
Full time Data Science Data Engineering Data Analytics Big Data Data Management Statistics Data Governance

Job Description

Want to build a stronger, more sustainable future and cultivate your career? Join Cargill's global team of 155,000 employees who are committed to safe, responsible and sustainable ways to nourish the world. This position is in Cargill’s agricultural supply chain business, where we seamlessly connect farming customers with food, feed and industry.
Job Purpose and Impact

The Systematic Trading Data Analyst will apply new, complex models and globally integrated perspectives to generate strategic insights that will promote the company's competitive position and ultimately grow trading earnings. In this role, you will be a consumer of data working across product lines and will have an important role in the evaluation of complex new datasets and new sources of data. You will participate in the design and implementation of application programming interfaces and data access queries by providing requirements for advanced models or visualizations and you will be a resource for other analytical and product line trading teams. Key Accountabilities

Validate quality and coverage of datasets and proposes new datasets to be captured working with other analytical teams and leadership to identify and prioritize broadly defined data and analytical tool development efforts based on trading opportunity.
Be accountable for the full supply chain for data from acquiring it in databases to supporting it with live trading standards. Build the overall audit tracing and alarm systems around it. Always challenge data.
Help with supporting the daily life of the systematic trading desk, in particular monitoring and troubleshooting the systems and execution
Deploy a comprehension of the trading questions that data can help to solve.
Partner and consult with regional and global analytical teams to ensure we gather, centralize and use the most valuable data for analysis in a systematic, repeatable and accurate manner.
Lead and collaborate across teams to define and design scalable and integrated data systems and processes with a focus on continuous improvement of analytical methods and tools. In particular, build understanding of data by communicating with data owners using data science models to get your own, independent quantitative approach and communicate with quant traders.
Independently handle complex issues with minimal supervision, while escalating only the most complex issues to appropriate staff.
Other duties as assigned Qualifications
Master's degree or other post-secondary degree, Doctorate degree Data Science, Computer Science, Math, Engineering.
Experience in a software development environment and code management.
Fluency in python, sql, github, clouds, servers…
Experience with, and understanding of, relational and distributed data systems
Advanced capabilities in operations research and solving complex business optimization problems
Minimum of four years of related work experience
Other minimum qualifications may apply PREFERRED QUALIFICATIONS
Familiarity with scientific programming languages
Ability to develop queries using multiple programming languages
10 years of experience working with data and data warehousing systems
Experience in commodities
Sees ahead to future possibilities and translates them into breakthrough strategies.
Hand-on deliveries under strict timelines
Ability to understand business requirements and serve as a consultant to internal teams
Makes sense of complex, high quantity, and sometimes contradictory information to effectively solve problems.

We provide a fast paced stimulating international environment, which will stretch your abilities and channel your talents. We also offer competitive salary and benefits combined with outstanding career development opportunities in one of the largest and most solid private companies in the world.

Interested? Then make sure to send us your CV and cover letter in English today:
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