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| Job Location | Gurugram |
| Education | Not Mentioned |
| Salary | Not Disclosed |
| Industry | IT - Software |
| Functional Area | Statistics / Analytics |
| EmploymentType | Full-time |
DescriptionAbout SirionLabs:SirionLabs - SaaS Product firm | is looking for people who simply put are driven to make a difference.SirionLabs is a growth stage company building breakthrough technology in Contract Lifecycle Management. We are hosted on AWS utilizing Aurora, Redshift, Lambda and are built on Java, C#, Golang, Angular, MongoDB and Postgres.We make data speak for itself by ingesting large volumes of machine data, support data and complex enterprise data sets. We rely on NLP (coreNLP), Neuralnets and Machine Learning technologies to improve operational efficiency and for predictive analysis.We are a team of high energy individuals who revel in a rapid-pace, agile product development environment. We aim to build path-breaking, context-aware products that solve meaningful and complex problems. In doing so, we hope to put Indian engineering on the world map. So, if these are the ideals that excite you, there is definitely a place for you at SirionLabs!Job Role: Manager Business AnalysisYears of Experience Required: 5+ yearsWork Location: GurgaonWhat you will do:1. Learn about the product in depth so that you can articulate the value to existing and new customersa. Invest time on an ongoing basis to keep yourself up-to-date on new developments2. Map different user types/ customers personas. Work closely with different users to understand the business decisions they make. Map those decisions to the information that can be provided to them in a seamless fashion through SirionLabs platform3. Understand other data sources that the product is integrated with or in a position to pull the data from so that we can provide seamless experience to the customers and
Keyskills :
equipment supplymapjavabusiness analyticsfashionrelationship buildingagileawssaasoperational efficiencybusinessmanagement consultingproduct developmentmongodbnlpquantitative data