Data Scientist

Responsibilities

• Designs, develops and programs methods, processes, and systems to consolidate and analyze structured/unstructured, diverse big data sources to generate actionable insights and solutions for client services and product enhancement.
• Builds "products" for Analysis.
• Interacts with product and service teams to identify questions and issues for data analysis and experiments.
• Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources.
• Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.
• Lead to the accomplishment of key goals across consumer and commercial analytics functions.
• Work with key stakeholders to understand requirements, develop sustainable data solutions, and provide insights and recommendations.
• Document and communicate systems and analytics changes to the business, translating complex functionality into business relevant language.
• Validate key performance indicators and build queries to quantitatively measure business performance.
• Communicate with cross-functional teams to understand the business cause of data anomalies and outliers.
• Develop data governance standards from data ingestion to product dictionaries and documentation.
• Develop SQL queries and data visualizations to fulfill ad-hoc analysis requests and ongoing reporting needs leveraging standard query syntax.
• Organize and transform information into comprehensible structures.
• Use data to predict trends and perform statistical analysis. Use data mining to extract information from data sets and identify correlations and patterns.
• Monitor data quality and remove corrupt data. Evaluate and utilize new technologies, tools, and frameworks centered around high-volume data processing.
• Improve existing processes through automation and efficient workflows.
• Build and deliver scalable data and analytics solutions.
• Work independently and take initiative to identify, explore and solve problems.
• Design and build innovative data and analytics solutions to support key decisions.
• Support standard methodologies in reporting and analysis, such as, data integrity, unit testing, data quality control, system integration testing, modeling, validation, and documentation.
• Independently support end-to-end analysis to advise product strategy, data architecture and reporting decisions.

Requirements

• Must have 8 YOE minimum as a senior level data scientist (or similar) from large scale enterprise level environments.
• Must be proficient/expert level in; SQL, Python, Apache Spark, AWS, and Databricks