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.