Data Scientist

Responsibilities • Investigate the feasibility of applying scientific principles to technologies, processes, and products. • Plan and execute research initiatives to validate concepts and solutions. • Build analytics tools leveraging data pipelines to deliver actionable insights on customer acquisition, operational efficiency, and business performance. • Participate in intellectual property evaluations and support patent development activities. • Collaborate with internal and external subject matter experts to enhance research outcomes. • Partner with stakeholders across the organization to identify data-driven business opportunities. • Mine and analyze large datasets to optimize product development, clinical marketing, and business strategies. • Evaluate the effectiveness and accuracy of new data sources and data collection methodologies. • Develop and deploy custom data models, algorithms, and predictive analytics solutions. • Implement predictive modeling techniques to optimize customer experience, revenue generation, and targeting strategies. • Collaborate with cross-functional teams to deploy models and monitor performance outcomes. • Develop tools and processes to track model performance, data accuracy, and system reliability. • Support data infrastructure needs and resolve data-related technical challenges. • Ensure data security and compliance across multiple data centers and cloud environments (AWS/Azure). • Build and enhance tools for analytics and data science teams to improve productivity and innovation. • Collaborate with data and analytics teams to continuously improve system capabilities. • Ensure adherence to quality systems and compliance requirements in all deliverables. Qualifications • Bachelor s degree with 9+ years of experience, or Master s degree with 6+ years of experience. • Proven experience building and deploying production-grade Retrieval-Augmented Generation (RAG) and Large Language Model (LLM) systems. • Strong expertise in evaluating retrieval quality, orchestration, cost optimization, and observability for AI systems. • Experience in complex classification problems with overlapping labels and regulatory implications. • Ability to work with cross-functional stakeholders including clinical SMEs, Quality, and IT/Security teams. • Strong programming skills in Python and SQL. • Experience with vector databases and cloud platforms such as AWS or Azure. Skills • Advanced analytical and problem-solving capabilities. • Strong understanding of data modeling, machine learning, and AI system design. • Experience with data pipelines, ETL processes, and large-scale data processing. • Ability to translate complex technical concepts into business insights. • Strong communication and stakeholder management skills. • Expertise in model evaluation, error analysis, and performance optimization. Functional Knowledge • Comprehensive technical expertise in data science, machine learning, and analytics. • Ability to recommend and implement improved processes across teams. Business Expertise • Strong understanding of industry best practices and business integration of data solutions. • Ability to drive business outcomes through data-driven strategies. Leadership • Mentor junior team members and provide technical guidance. • Lead cross-functional projects with moderate complexity and risk. Problem Solving • Solve complex problems using advanced analytical techniques and innovative approaches. • Apply critical thinking to evaluate multiple data sources and solutions. Impact • Drive improvements in business performance, operational efficiency, and customer outcomes. • Influence key organizational objectives through data-driven insights. Interactions And Communication • Communicate complex insights clearly to both technical and non-technical stakeholders. • Lead discussions and presentations to align cross-functional teams and drive decision-making.