Data Scientist and Analytics Specialist Job For 5-10 Year Exp In Bengaluru / Bangalore, Hyderabad / Secunderabad – 1439692

Data Scientist and Analytics Specialist
a) Perform sample data exploration;
b) Build model prototypes using sample data;
c) Define data requirements;
d) Work within business team establish framework for success and to design prototypes;
e) Liaise with machine intelligence and analytics teams to ensure model validation.
1. Demonstrate the ability to work with different teams for the purpose of delivering an overall digital solution, whether it be for web, mobile, or in-store;
2. Demonstrate expertise in the field of data science and analytics;
3. Demonstrate the ability to audit the quality of customer data, which originates from many different sources, and provide recommendations on improving the quality of data streams ingested by different company departments
4. Provide analysis of data that comes in different formats, including structured, numeric data in traditional databases to unstructured text documents, email, video, audio;
5. Provide expertise in working with Adobe Analytics and Google Analytics;
6. Demonstrate understanding different dimensions of big data relevant to our customers:
a. Variability: Understanding the nature of increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Provide high quality analysis and recommendations based on daily, seasonal, and event triggered peak data loads to manage;
b. Complexity: Understand the multiple sources of data and assist the organization to connect and correlate different enterprise data relationships;
c. Variety – Have the capability to process different types of data from XML to video to SMS;
d. Velocity – Ensure that data is accessible in real-time and near real-time;
e. Visualization – Have the capability to ‘show’ data in support of analyzing it;
7. Demonstrate to the the organization a strong understanding of the Hadoop and Micro strategy 10 programming framework;
8. Provide guidance on the intersection of data and artificial intelligence and how the organization could use them for strategic decision making; and
9. Provide guidance in how to implement and support deep learning tools and how to continue to support them. This component should also look at how the organization defines KPIs in determining the tools’ effectiveness.