Data Scientist

Data ScientistChoosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Job Location: Cary, NC (Onsite/Hybrid from Day 1)We are seeking an experienced Data Scientist to join our growing Analytics and AI team. This role is responsible for developing and deploying advanced Machine Learning, Artificial Intelligence, and Generative AI solutions that drive customer engagement, marketing effectiveness, revenue growth, and business outcomes. The ideal candidate will have a strong blend of technical expertise, business acumen, and stakeholder management experience, particularly within the insurance and/or financial services industry.Key ResponsibilitiesOwn technical decisions, project outcomes, timelines, and production stability within assigned business domains.Design, develop, train, and optimize machine learning and deep learning models for marketing, customer engagement, sales, and business analytics use cases.Analyze complex datasets to identify trends, patterns, anomalies, and actionable insights that support business strategy and decision-making.Develop statistical models, predictive analytics, and machine learning algorithms using Python and Azure cloud technologies.Build, deploy, and support production-ready ML and GenAI solutions, including API-based, batch, and real-time inference applications.Collaborate with business stakeholders, product teams, and cross-functional partners to identify opportunities and implement data-driven solutions.Integrate AI and ML capabilities into business applications and workflows through APIs, SDKs, and microservices.Create compelling visualizations, dashboards, reports, and presentations to communicate analytical findings and recommendations to senior leadership and business partners.Apply MLOps best practices to ensure scalability, reliability, performance monitoring, and operational excellence of AI/ML solutions.Optimize platform components leveraging cloud-native architectures, distributed computing, and efficient resource management practices.Stay current with emerging trends, technologies, and best practices in Artificial Intelligence, Data Science, Machine Learning, and Generative AI.Promote responsible AI practices, including data privacy, model governance, bias mitigation, and model monitoring.Required QualificationsBachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field.8+ years of experience in Data Science, Machine Learning, Artificial Intelligence, and/or AI/ML Engineering.5+ years of experience in the Insurance and/or Financial Services industry with exposure to sales, marketing, customer engagement, or customer analytics.Proven experience designing, deploying, and operating production Machine Learning and/or Generative AI solutions, including APIs, batch processing, and real-time inference.Strong experience developing Machine Learning models using Python, preferably in cloud environments.Experience with Azure Machine Learning, Domino Data Lab, Power BI, or similar analytics and ML platforms.Strong SQL skills and hands-on experience with data analysis, anomaly detection, data validation, and Exploratory Data Analysis (EDA).Solid understanding of statistics, mathematics, predictive modeling, machine learning algorithms, and data science methodologies.Experience leveraging AI and predictive analytics to improve customer experience, communication strategies, revenue generation, marketing effectiveness, and other business outcomes.Familiarity with responsible AI principles, including data privacy, bias mitigation, model explainability, governance, and monitoring.Excellent written and verbal communication skills, including the ability to present insights effectively through storytelling, visualizations, and executive-level presentations.Preferred QualificationsExperience with Generative AI, Large Language Models (LLMs), and modern AI frameworks.Experience implementing MLOps and model lifecycle management practices in cloud environments.Knowledge of cloud-native architectures and scalable AI/ML deployment patterns.Experience working within highly regulated environments such as Insurance or Financial Services.