Data Scientist and Data Engineer

The Volkswagen Data Lab wants to develop and test IT solutions for the digital future of the VOLKSWAGEN GROUP with the target of IT innovation leadership, Development and piloting of new technologies, Cooperation with the most innovative technology companies worldwide, Reduction of dependence on external service providers & agencies and Building strategic partnerships.

• Patents: Submission of 9 Invention disclosures and 3 are in the process of Patent filing
• After Sales: Development of intelligent algorithms for repeat repairs in after sales, dealer fraud detection and dealer comparisons
• After Sales: Clustering on early defect warning system
• After Sales: Comparative analysis between different companies in After-Sales business
• After Sales: Clustering the customers and dealers in After-Sales business
• Connected Car: Analysis of connected car data especially weather relevant vehicle data like temperature, sun intensity, rain and brightness to build a map layer with real-time aggregated values which satisfies GDPR Anonymization, and provide these values to Energy Providing Companies, which helps in better planning their energy production
• Connected Car: Analysis of Telematics data to prepare connected car project for identification of relevant dealers' location based on customer driving behavior
• Power Plants: Electricity Demand prediction for Power Plants
• Sales: Predicting car sales and their configurations using machine learning techniques
• Finance: Finance Margin Optimization of Trucks using machine learning techniques
• Marketing: Analysis of customer visits to the workshop before and after recall campaign
• Security: Developed an application for Car Theft Protection
• Production: Digitalisation of the Production Platforms especially in Optimizing OEE (Overall Equipment Efficiency)
• Adhoc: Sales, Configurations and Social Media Analysis on Abgas Topic
• Technical: Built an On-Premise based Data Lake Architecture which is implemented for 2 projects
• Technical: Built a Cloud based Data Lake Architecture, which is implemented as Digitalisation Platform for Cross-Functional applications. This project was implemented with the help of Agile methods
• Technical: Visualization of the end results of the project
• Technical: Technology Scouting
• Technical: Infrastructure Management
• Technical: Data Management Support
• Technical: Data Extraction, Transformation and Load
• Technical: Learning New Data Technologies
• Technical: Conducting workshops for the team members on new technologies
• Technical: Taking Care of Technology Licenses
• Technical: Data Security Management
• Technical: Testing new technologies whether it is suitable for Data Lab or not and documenting the same in internal Tech-Library
• Technical: Data Lab Application Management
• Technical: Installation of new technologies

Bell Helicopter Textron plans to put in place functions that will enable them to achieve the desired level of corporate performance for its key business processes. This includes putting in place robust & accurate performance reporting and enabling key performance management processes including Manufacturing, PLM & Project Management Systems.

• Development of data analytical database
• Creation and execution of the Jobs
• Creation of the tables based upon the flat file structure
• Responsible for all offshore activities
• Communication to Onsite coordinators and to the Downstream

Objective to develop Finance Business Layer is to have a common Database layer that which will be a single source of truth for all the transactional applications that are being used in Finance IT. This is a 2 step process, where in Step 1, a staging layer will be created which will extract tables on a as-is format from different source systems. Step 2 is to create a business layer where the tables will be built in based on the business requirement of the transactional applications those will be the consumer of FBL.

• Data mining on finance business layer
• Carrying out specified data processing and statistical techniques
• Creation and execution of the ETLs
• Proper ETL design documentation
• Responsible for all offshore activities (India)
• Performing all the QA move checks
• Identifying the usage of various indexes and using them appropriately
• Migration of code to QA from Development
• Performance tuning of the history ETLs by the concept of partitioning

EDW2B, the next generation Data Warehouse (EDWTD) of Cisco is built in Teradata platform with 3NF data model. The Cisco Data Warehouse (CDW) now the Enterprise Data Warehouse (EDW), has evolved through an Enterprise Architecture initiative to enable subscribing systems to report consistent numbers as recipients of data from the EDW. This initiative is ERBI-Enterprise Reporting Business Intelligence, which enables reporting applications to receive "Single Source of Truth" SSOT, measures from EDW. Data warehouses can enable subscribing systems to report consistent numbers only if they are all pointing to the data warehouse. The intention of EDWTD is to act as a state of art data warehouse which is near real time and SSOT for different transactions.

• Performance Tuning
• Answered end users for Adhoc requests
• Did data reconciliation in Teradata across multiple databases
• Involved in writing scripts using Teradata SQL and ANSI SQL as per requirements
• Check Latency issues and Environment Sanity checks