Data Scientist

Job Description:

• Understand and prioritize business problems and identify ways to leverage data to recommend solutions to business problems. Organize and synthesize data into actionable business decisions, focused on insights. Provide insight into, trends, financial and business operations through data analysis and the development of business intelligence visuals.
• Work with advanced business intelligence tools to complete complex calculations, table calculations, geographic mapping, data blending, and optimization of data extracts.
• Apply all Phases of Software Development Life Cycle  using Waterfall and Agile methodologies
• Proficient in working on Apache Hadoop ecosystem components like Map-Reduce, Hive, Pig, SQOOP, Spark, Flume, HBase and Oozie with AWS EC2/Azure VM’s cloud computing.

Requirement:

• Strong hands-on experience in Spark, Scala, R, Python, and/or Java.
• Programming experience with the Hadoop ecosystem of applications and functional understanding of distributed data processing systems architecture (Data Lake / Big Data /Hadoop/ Spark / HIVE, etc).
• Amazon Big Data ecosystem (EMR, Kinesis, Aurora, DynamoDB etc…) experience
• Microsoft Azure Data ecosystem (Databricks, Stream analytics, Purview, Synapse Analytics etc.)
• Proficient in working with AWS Sagemaker and Azure ML for building AI/ML Models.
• Extensive skills in Communication and Collaboration Skills
• Must be able to convey key messages in technical terms and business terms. Must be able to create technical documentation, such as specifications, design documents, and testing documents.
• Oral: Ability to collaborate and communicate with a wide range of partners, including IT and business, across all levels of the organization. Must actively manage expectations with stakeholders.
• Problem Solving: Must understand the business need and develop technical solutions to meet those needs. Innovation, creativity, and critical problem-solving skills are required to be successful in this role. Solutions need to be comprehensive, flexible for future changes, and delivered with a high degree of quality.

Key Skills:

• Statistical analysis and computing.
• Machine Learning.
• Deep Learning.
• Processing large data sets.
• Data Visualization.
• Data Wrangling.
• Mathematics.
• Programming.