S&P Global Ratings is looking for an experienced Data Science Engineer to join Data Engineering team within Chief Data Office, a team of data and technology professionals who define and execute the strategic data roadmap for S&P Global Ratings. The successful candidate will participate in the design and build of S&P Ratings cloud based analytics platform to help develop and deploy advanced analytics/machine learning solutions. The TeamYou will be an expert contributor and part of the Rating Organization s Data Services Team. This team, who has a broad and expert knowledge on Ratings organization s critical data domains, technology stacks and architectural patterns, fosters knowledge sharing and collaboration that results in a unified strategy. All Data Services team members provide leadership, innovation, timely delivery, and the ability to articulate business value. Be a part of a unique opportunity to build and evolve S&P Ratings next gen analytics platform. Our Hiring Manager SaysIf you are an individual that brings demonstrated experience of delivering big data projects as a data science engineer,, this is an excellent opportunity. I am looking for someone with sound technical knowledge, can be hands-on, worked on transformational initiatives, and can drive results. Responsibilities: Design and develop efficient and scalable data pipelines between enterprise systems and analytics platform Work closely with Data Science team and participate in development and deployment of machine learning models and feature engineering pipelines Provide technical expertise in the areas of design and implementation of Ratings Integrated Data Facility with modern AWS cloud technologies such as S3, Redshift, EMR, Hive, Presto and Spark Build and maintain a data environment for speed, accuracy, consistency and up time Support analytics by building a world-class data lake environment that empowers analysts to determine insights into revenue and power products across the organization Work with the machine learning engineering team to build a data eco system that supports AI products at scale Ensure data governance principles adopted, data quality checks and data lineage implemented in each hop of the data Partner with the chief data office, enterprise architecture organization to ensure best use of standards for the key data domains and use cases Be in tune with emerging trends Big data and cloud technologies and participate in evaluation of new technologies Ensure compliance through the adoption of enterprise standards and promotion of best practice / guiding principles aligned with organization standards Experience & Qualifications: BS or MS degree in Computer Science or Information Technology 8+ years of experience as data engineer at an innovative organization 4+ years of hands-on experience in implementing data lake systems using AWS cloud technologies such as S3, Redshift, EMR, Hive, Presto and Spark Expert managing AWS services (EC2, S3, Route 53, ELB, VPC, cloudwatch, Lambda) in a multi account production environment Experience With Machine Learning Frameworks, such as TensorFlow , PyTorch, H2O, scikit-learn, Theano, Caffe or Spark MLib is an added advantage Exposure to R, SparkR, SparklyR or Other R packages is a plus Experience in constructing fast data staging layers to feed machine learning algorithms Experience in building data APIs to consume analytic model output Familiarity with machine learning model training and deployment process is a plus Experience with development frameworks as well as data and integration technologies such as Python, Scala or Informatica Expert knowledge of Agile approaches to software development and able to put key Agile principles into practice to deliver solutions incrementally. Monitors industry trends and directions; develops and presents substantive technical recommendations to senior management Excellent analytical thinking, interpersonal, oral and written communication skills with strong ability to influence both IT and business partners Ability to prioritize and manage work to critical project timelines in a fast-paced environment Financial services industry experience Associated topics: data architect, data center, data integration, data integrity, data quality, data warehousing, database, database administrator, sybase, teradata
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