Principal Data Scientist

Overview Do you enjoy solving problems, looking at problems through a different lens, and working closely with customers to innovate new solutions to complex problems? Do you jump with excitement at the opportunity to identify trends and provide unique business solutions? Do you want to join a team where learning about a new technology or solution is part of our work every day? The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global consulting and engineering organization that supports our most complex and leading-edge customer engagements. Driving early-stage deliveries, enhances ISD’s technical capabilities, and partnering with others to develop approaches, innovative solutions, and engineering standards in order to set our sales and delivery teams up for success. Leveraging the principles of model, care, and coach, we provide consistent high-quality customer experience through technical and AI leadership and IP capture centered on delivery truth. Responsibilities Business Understanding and Impact Leverages subject matter expertise to analyze problems and issues facing projects to uncover, manage, and/or mitigate factors that can influence final outcomes across product lines. Partners with business team to drive strategy and recommend improvements. Raises opportunities to look for new work opportunities and different contexts to use existing work. Establishes, applies, and teaches standards and best practices. Design AI solutions to meet the complex business requirements. Data Preparation and Understanding Oversees data acquisition efforts and ensures data is properly formatted and accurately described. Utilizes key technologies and tools necessary for data exploration (e.g., structured query language (SQL), Python). Uses querying, visualization, and reporting techniques to explore the data, including distribution of key attributes, relationships between attributes, simple aggregations, properties of significant sub-populations, and statistical analyses. Mentors and coaches engineers in data cleaning and analysis best practices. Identifies gaps in current data sets and drives onboarding of new data sets (e.g., bringing on third‑party data sets). Drives discussions around ethics and privacy policies related to collecting and preparing data. Integrates industry-wide ethics insights and best practices to influence internal processes and drive decision‑making. Builds data platforms from scratch across products Builds data‑science business solutions using existing technologies, products, and solutions, as well as established patterns and practices. Provides guidance on model operationalization of models created by data scientists. Identifies new opportunities from data and processes data in a way that is usable for general purpose. Actively contributes to the body of thought leadership and intellectual property (IP) on best practices for data acquisition and understanding. Leads and resolves data‑integrity problems. Modeling and Statistical Analysis Generalizes machine learning (ML) solutions into repeatable frameworks (e.g., modules, packages, general‑purpose software) for others to use. Exemplifies and enforces team standards related to bias, privacy, and ethics. Evaluates the methodology and performance of teammates’ models and, as appropriate, recommends solutions for improvement. Anticipates the risks of data leakage, the bias/variance tradeoff, methodological limitations, etc., and is able to guide teammates on solutions. Drives best practices relative to model validation, implementation, and application. Develops operational models that run at scale. Partners with others to identify and explore opportunities for the application of ML and predictive analysis. Identifies new customer opportunities for driving transformative customer solutions with ML modeling. Incorporates best practices for ML modeling with consideration for artificial intelligence (AI) ethics. Develops deep expertise in specialized areas by staying abreast of current and emerging methodologies an AI and ML. Evaluation Conducts thorough review of data analysis and modeling techniques used to summarize the process review and highlight areas that have been missed or need reexamining. Utilizes results of the assessment and process review to decide on next steps (e.g., deployment, further iterations, new projects). Identifies new evaluation approaches and metrics and invents new methodologies to evaluate models. Industry and Research Knowledge/Opportunity Identification Tracks advances in industry and academia, identifies relevant state‑of‑the‑art research, and adapts algorithms and/or techniques to drive innovation and develop new solutions. Researches and maintains deep knowledge of industry trends, technologies, and advances. Leverages knowledge of work being done on team to propose collaboration efforts. Proactively develops strategic responses to specific market strengths, weaknesses, opportunities, threats, and/or trends. Mentors and coaches less experienced engineers in data analysis best practices. Serves as subject matter expert and role model for less experienced engineers. Identifies strategy opportunities. Actively contributes to the body of thought leadership and intellectual property (IP) best practices by actively participating in external conferences. Coding and Debugging Independently writes efficient, readable, extensible code/model that spans multiple features/solutions. Contributes to the code/model review process by providing feedback and suggestions for implementation and improvement. Develops expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. Leads a project team in the gathering, integrating, and interpreting of data/information from multiple sources in order to properly troubleshoot errors. Provides feedback on non‑optimized features/solutions back to product group, and explores potential for new features. Leverages expert‑level proficiency of big‑data software engineering concepts, such as Hadoop Ecosystem, Apache Spark, continuous integration and continuous delivery (CI/CD), Docker, Delta Lake, MLflow, AML, and representational state