Data Scientist

The core responsibilities for the job include the following:

Leadership and Delivery:

Lead the architecture, development, deployment, and governance of enterprise AI/ML solutions.

Lead complex data science projects end-to-end, delivering significant and measurable business impact.

Define technical standards, reusable frameworks, and best practices for AI and machine learning development.

Design and implement enterprise-wide data science frameworks, governance models, and best practices.

Mentor and guide data scientists while promoting technical excellence.

Advanced Analytics and AI:

Solve highly complex business problems using machine learning, statistical modeling, generative AI, and advanced analytics.

Design and implement agentic workflows and multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, and AWS AgentCore.

Develop AI-powered solutions, including workflow copilots, intelligent automation agents, content generation systems, and code assistants.

Drive AI governance, explainability, monitoring, validation, and responsible AI practices.

Stakeholder and Strategy:

Partner with business leaders, clinical experts, statisticians, and technology teams to identify AI opportunities.

Work closely with senior leadership to inform and influence strategic business decisions through data-driven insights.

Translate complex analytics into actionable business recommendations.

Define and execute the roadmap for AI, machine learning, and advanced analytics capabilities.

Technical Leadership:

Design scalable AI architectures supporting large structured and unstructured datasets.

Lead technology selection, solution design, model deployment, and continuous improvement initiatives.

Evaluate emerging technologies and drive innovation across the organization.

Requirements:

Master's or PhD in data science, computer science, statistics, artificial intelligence, mathematics, bioinformatics, or a related discipline.

8-12 years of experience in data science, AI, machine learning, or advanced analytics.

Proven experience leading AI/ML initiatives from ideation through production deployment.

Extensive experience leading complex data science projects from end-to-end and driving significant business impact.

Proven track record of working closely with senior leadership to inform and influence high-level strategic business decisions.

Demonstrated experience in designing and implementing enterprise-wide data science frameworks and best practices.

Experience mentoring teams and providing technical leadership.

Experience in pharmaceutical, life sciences, healthcare, or other regulated industries preferred.

AI, ML, and Generative AI:

Expertise in machine learning, deep learning, NLP, predictive modeling, and statistical analysis.

Deep knowledge of LLMs, prompt engineering, fine-tuning, agentic AI, and AI application architecture.

Experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, MCP, and AWS AgentCore.

RAG and Knowledge Graphs:

Hands-on experience with RAG, Agentic RAG, GraphRAG, Hybrid Search, Vector Databases, and Semantic Retrieval.

Experience with Neo4j, knowledge graphs, ontologies, and enterprise search platforms.

Programming and Engineering:

Expert proficiency in Python.

Strong experience with R; SAS knowledge preferred.

Knowledge of APIs, microservices, CI/CD, GitOps, testing frameworks, and software engineering best practices.

Cloud, MLOps, and LLMOps:

Experience with AWS, Azure, or GCP.

Strong understanding of MLOps/LLMOps, model governance, monitoring, automation, Docker, and Kubernetes.

Visualization and Communication:

Experience with Tableau, Power BI, R Shiny, Plotly, or equivalent visualization tools.

Strong storytelling, presentation, and executive communication skills.

Additional Qualifications:

Strong leadership, mentoring, and stakeholder management capabilities.

Ability to balance strategic thinking with hands-on execution.

Excellent communication and influencing skills.

Proven innovation mindset and cross-functional collaboration skills.

Experience supporting GxP-compliant systems and regulatory requirements is highly desirable.