Overview

Position Summary: The Senior Data Scientist is responsible for transforming complex data into business value through advanced analytics, artificial intelligence (AI), and machine learning (ML). This role leads the design, development, and deployment of scalable analytical solutions that solve high-impact business problems and enable data-driven decision-making. Responsibilities include evaluating new data sources, developing predictive models, statistical algorithms, AI/ML solutions, and proof of concepts, and translating research into production-ready capabilities. The Senior Data Scientist I builds data pipelines and analytical datasets for modeling and AI applications while ensuring scalable, reliable solutions. This role also provides technical leadership, provides guidance to junior data scientists, collaborates with cross-functional teams, and leads multiple medium- to large-scale projects from concept through implementation, delivering measurable business outcomes. Major Responsibilities: Data Preparation & Engineering - Identify source data and build data models to solve complex business problems. - Extract, transform, and prepare structured and unstructured data. - Collaborate with IT to build scalable data pipelines and analytical datasets for modeling and analytics. - Clean, validate, and monitor data quality. - Perform feature engineering and exploratory data analysis. - Develop data quality metrics and visualizations to identify trends and opportunities. Data Science, Machine Learning & Advanced Analytics - Design, develop, deploy, and monitor statistical and machine learning models. - Select appropriate statistical methods, modeling techniques, and algorithms. - Design and evaluate predictive analytics solutions. - Conduct statistical analyses and experiments to validate hypotheses and support business decisions. - Develop prototypes and proof of concepts. - Operationalize analytical solutions for production use. - Support the AI/ML lifecycle by monitoring model performance and driving continuous improvement. - Monitor model performance, address model drift, and improve business outcomes. - Apply advanced analytical techniques, including forecasting, classification, clustering, and natural language processing (NLP), and Sentiment Analysis. - Apply AI techniques, including generative AI, agentic AI, and large language models (LLMs), where appropriate to enhance analytical solutions. Technical Leadership & Solution Delivery - Lead multiple medium- to large-scale data science initiatives from concept through implementation. - Translate business challenges into analytical solutions. - Define technical approaches and project methodologies. - Establish best practices for data science development, documentation, and model governance. - Partner with Enterprise Engineering, Advanced Analytics, HRIT, and other cross-functional teams to deliver enterprise solutions. - Mentor and provide technical guidance to junior data scientists. - Evaluate emerging technologies and recommend improvements to analytical solutions and data science practices. Business Partnership & Communication - Partner with business leaders and subject matter experts to identify data-driven opportunities. - Communicate analytical methods, insights, and recommendations to technical and non-technical audiences. - Present findings through effective data storytelling and visualization. - Measure and communicate the business impact of analytical solutions using appropriate performance metrics. - Create clear technical documentation and project deliverables. - Facilitate discovery sessions and support solution design. Innovation & Continuous Learning - Stay current with advances in data science, machine learning, AI, and cloud technologies. - Build expertise in business domains and enterprise data assets. - Promote responsible AI through fairness, transparency, and explainability. - Share knowledge and best practices across the data science community. - Identif