Strategy & Analytics
Our Strategy practice brings together several key capabilities that will allow us to architect integrated programs that transform our clients’ businesses, including Corporate & Business Unit Strategy, Technology Strategy & Insights, Enterprise Model Design, Enterprise Cloud Strategy and Business Transformation.
Strategy professionals will serve as trusted advisors to our clients, working with them to make clear data-driven choices about where to play and how to win, in order to drive growth and enterprise value.
Strategy will help our clients:
Identify strategies for growth and value creation
- Develop the appropriate business models, operating models, and capabilities to support their strategic vision
- Maximize the ROI on technology investments and leverage technology and Cloud trends to architect future business strategies \
Analytics & Cognitive
In this age of disruption, organizations need to navigate the future with confidence, embracing decision making with clear, data-driven choices that deliver enterprise value in a dynamic business environment.
The Analytics & Cognitive team leverages the power of data, analytics, robotics, science and cognitive technologies to uncover hidden relationships from vast troves of data, generate insights, and inform decision-making. Together with the Strategy practice, our Strategy & Analytics portfolio helps clients transform their business by architecting organizational intelligence programs and differentiated strategies to win in their chosen markets.
Analytics & Cognitive will work with our clients to:
Implement large-scale data ecosystems including data management, governance and the integration of structured and unstructured data to generate insights leveraging cloud-based platforms
Leverage automation, cognitive and science-based techniques to manage data, predict scenarios and prescribe actions
Drive operational efficiency by maintaining their data ecosystems, sourcing analytics expertise and providing As-a-Service offerings for continuous insights and improvements
ML Data Engineer Required:
1.5-9 years of Consulting, Data, and Analytics experience
- Experience in building data pipelines and assembling large & complex datasets for ML models
Experience in Python, PySpark, Scala, Hive and SQL
Ability to work across structured, semi-structured, and unstructured data, to develop extracts and linkages
Experience in any of the cloud-based data platforms like AWS, GCP and Azure
Strong logical structuring and problem-solving skills
Strong verbal, written and presentation skills
Meaningful experience in multiple database technologies such as Distributed Processing (Spark, Hadoop, EMR, BigQuery), traditional RDBMS (Oracle, MySQL, PostgreSQL), MPP (AWS Redshift, Teradata), NoSQL (MongoDB, Cassandra, Neo4J, Titan)
Experience with real time data movement solutions that use security and encryption protocols while data is in transit
Experience with state-of-the-art machine learning models, NLP, Statistical inferences, Regression, GLM, Random forest, Boosting, Text Mining and Social Network analysis.
Experience in Application Development or Data-Warehouse, across technologies used in the enterprise space