Guavus provides dynamic solutions for data-rich businesses, so that they can gain competitive advantage in creating more meaningful customer experiences.
An Analytics Engineer at Guavus is responsible for analyzing extremely large volumes of data and turning that data into actionable insights for timely decision making by our customers. A person in this role will be responsible for building statistical models from multiple data sources in order to provide advanced analytics and reporting. The ideal candidate should have skills to quickly do a literature survey, perform proof of value using known statistical techniques and write production quality code to move the research into production. We are looking for a person with exceptional coding skills along with decent analytics and statistical skills to support our business. The role currently helps our customers – primarily large telecom service providers – get insights to increase revenues or reduce costs by better network planning, customer churn management, service and cloud prospecting, application performance analysis, sales prospecting, etc. This is an exciting, technical role that is focused on enabling existing and new customers to create real, practical value through advanced analytics on their streaming data. You will work closely with our team of scientists and engineers in providing cutting edge analytics solutions to customers worldwide.
- Work with terabytes of structured and semi-structured data sets
- Do a literature survey and identify state of the art techniques to solve the problem
- Quickly design and conduct experiments to showcase proof of value.
- Build predictive models using R / Python and then translate the code into Java / Scala.
- Work closely with other team members in converting the business problem definition into validated results with customers and updating the core machine learning functionalities in our product lines.
- Must be self-driven and capable of prioritizing, organizing, and managing a substantial workload.
- The ideal candidate will have a strong academic background with strong Hands on Knowledge of Java, R, Python or Scala (a big plus).
- Good understanding of data structures and design patterns
- 2 – 4 years of experience in analyzing large volumes of data in building predictive models is desirable
- Exposure to data mining techniques such as outlier detection, classification, regression, prediction, hypothesis testing will be a big plus.
- Hands on knowledge of Map-Reduce, Hadoop and Spark will be a big plus
B.Tech / B.E / M.Tech/ MSC / MCA/ PhD in Computer Science, Engineering, Mathematics or other natural science field with a dissertation supported by data analysis.