The Analytics Enablement team is part of Corporate Center’s Analytics Center of Expertise. Its mission is to lead KeyBank’s journey to becoming an innovative, insights-driven enterprise organization by helping to build and grow a high-performing analytics community. Our experienced team of practitioners focuses on engaging, enabling, and empowering decision-makers across the enterprise by managing and supporting contemporary analytics tools, by developing and delivering robust training and development, by providing strong metadata management, and by researching, specifying, and deploying next-generation analytics capabilities. We are an internal consulting and services organization that works directly with users across the firm, including our partners in Enterprise Strategy, Data and Architecture, to facilitate the development of innovative solutions that help us compete and win with analytics.

The Data Science Software Engineer fills a critical technology support and innovation role for the analytics and advanced analytics functions within the organization. The Engineer is primarily responsible for end-user technical support and training in the use of open source technologies. S/he is also the primary conduit for identifying, researching, and evaluating new and innovative technologies that enhance the organization’s enterprise analytics and advanced analytics capabilities.

The Data Science Software Engineer works both independently and in collaboration with a cross-functional team of solution architects to effectively monitor, manage, and support analytics and advanced analytics technology, data infrastructure, and underlying analytics use cases—primarily focused around open source technologies. This individual evaluates short-term business needs required to support key business goals and priorities and works to ensure analytics solutions are built and deployed in an effective and efficient manner. Under the guidance of the Center’s Director and in cooperation with our partners in BI Infrastructure, the Engineer will coordinate the development of on-premise and cloud-based analytical infrastructure and tools providing computational and statistical capabilities to enhance business results and decision-making. The Engineer participates with end users, project leadership, and business partner in supporting the design of experiments and analytics; data sampling and mining, verification of data quality and information integrity, and best practices around the development of predictive models and data visualization solutions.The Engineer has responsibility for advising data scientists, project teams, and solution architects in the integration of analytical methods into decision support solutions and will assist peers in best practices and the selection and integration of appropriate tools to support required analytic. Close coordination with the organization’s business intelligence data engineers, data architects, and data integration (ETL) developers, and data science community is required to ensure tight integration of functionality and toolsets.

• Advanced degree in Computer Science, Management Information Systems or Decision Science or equivalent experience required.
• 3-5 years of hands-on experience in advanced analytics, data science, or open source networking and computing infrastructure
• Certified Professional Google Data Engineer preferred
• Candidate must be a self-starter and creative problem-solver with an innovative and curious mindset.
• Solid knowledge of data warehousing such as Hadoop, MapReduce, HIVE, PIG, Apache Spark, Kafka as well as cloud base data storage: Azure BLOB storage, AWS S3, Google Cloud Storage with various formats (Parquet, JSON, ORC, Avro, delimited)
• Solid understanding of databases such as DB2, Oracle, Teradata, MySQL, PostgreSQL
• Extensive Experience with R and Python including language-specific and data science-orientied packages required.
• Experience with SparkSQL, Spark MLlib, and other third-party machine learning algorithms such as XGBoost and LightGBM through the use of Scala, Pyspark and/or SparkR required
• Experience with SAS helpful
• Experience with Linux/Unix required
• Demonstrated knowledge of Web technologies
• Must have a working knowledge of advanced technology uses cases in financial services including blockchain, machine learning, interactive data visualization, natural language processing query, elastic search, cloud computing, and streaming analytics.
• Strong communication skills and the ability to interact and collaborate with all levels of the organization.
• A broad, enterprise-wide view of the business and varying degrees of appreciation for strategy, processes and capabilities, enabling technologies, and governance
• The ability to recognize pain points within the organization, functional interdependencies and cross-silo redundancies. Those issues may exist in role alignment, process gaps and overlaps, and business capability maturity gaps
• The ability to apply architectural principles, methods, and tools to business challenges
• The ability to create capability portfolios and technical roadmaps addressing gaps
• The ability to understand and recognize the economics of technology and the business goals
• The ability to perform industry analysis and identify business and technology trends specific to the portfolio
• The ability to visualize and create high-level models that can be used in future analysis to extend and mature the business architecture
• The ability to assist business case creation and realization by aligning business goals to organizational capabilities
• Strong situational analysis and decision-making abilities
• Financial and/or Banking background preferred.

FLSA STATUS:Exempt

KeyCorp is an Equal Opportunity and Affirmative Action Employer committed to engaging a diverse workforce and sustaining an inclusive culture. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.