User Documentation at Sigma
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Project scope
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Data analysis Product or service launch Software development Machine learning Artificial intelligenceSkills
software documentation machine learning customer success management counterparty risk user feedback user flows new product development salesAt Sigma, we are rolling out a machine learning-enabled feature to extract entity relationships from news media. The purpose of this feature is to give financial crime investigators and compliance professionals deeper insight into people or companies that they are researching.
Sigma360 is a counterparty risk intelligence platform designed to reduce the risk and cost of compliance and improve the speed of business for our clients. Sigma is a Series A company ramping up for Series B funding and poised for significant growth. See more about Sigma here.
Learners will work within the Product team to document a newly launched risk intelligence platform, Sigma360. The primary tasks of this project include:
- Document end user flows within the Sigma360 platform.
- Work with the Sales and Customers Success teams to identify and document FAQs.
Successful learners will have additional opportunity to grow within the Product team by:
- Documenting new product requirements and reviewing them with the development team.
- Triaging user feedback with the Customer Success team and ensuring requirements are entered into the product backlog.
Learners will report to Matt Monarch (Director, Data Products) throughout this project. Learners will be integrated into the existing Product team and will be supported as such.
About the company
Who We Are
Founder led, MIT-incubated company building definitive risk software for financial services, corporates and professional services firms
What We Do
Leverage deep risk and compliance domain expertise, global data and cutting-edge technology to deliver smarter, more complete financial crime and credit risk detection and decisioning solutions
How We Do It
Utilize proprietary entity resolution, matching and visualization techniques to connect external and internal data and create a unified, configurable view of risk on any entity
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