Job Description
Role Summary
The Senior Data Scientist Generative AI & Advanced Analytics is responsible for designing, developing, and deploying advanced machine learning and Generative AI (GenAI) solutions that solve complex business problems at scale. This role blends classical data science, modern ML engineering practices, and GenAI application development, with a strong focus on production readiness, explainability, and responsible use of AI.
The role partners closely with Analytics, Data Engineering, Cloud Engineering, and Product teams to translate business problems into AI driven solutions, including LLM based applications, agentic workflows, and predictive models.
Key Responsibilities
Advanced Data Science & Machine Learning
Design and develop predictive, classification, and optimization models using structured and unstructured data.
Perform exploratory data analysis and feature engineering to support high quality model development.
Evaluate model performance, robustness, and business impact using appropriate metrics.
Lead model experimentation and iteration from PoC to production.
Generative AI Solution Development
Design and build Generative AI applications, including:
LLM powered decision support tools
Retrieval Augmented Generation (RAG) solutions
Prompt engineered workflows and agentic systems
Fine tune or adapt large language models where appropriate using enterprise approved tools and data.
Develop and evaluate prompts, embeddings, and retrieval strategies to optimize GenAI outputs.
Integrate GenAI solutions into existing analytics platforms, workflows, or applications.
Production Deployment & MLOps
Package models and GenAI solutions for scalable deployment.
Partner with Cloud and ML Engineers to deploy models as services or APIs.
Implement monitoring for model performance, drift, and GenAI output quality.
Support lifecycle management, versioning, and retraining strategies.
Responsible AI & Governance
Ensure all AI and GenAI solutions comply with enterprise Responsible AI (RAI), privacy, and security standards.
Perform required performance, bias, and fairness testing prior to production deployment.
Document AI use cases, assumptions, limitations, and validation results for governance reviews.
Participate in AI review and approval processes using enterprise AI platforms.
Cross Functional Collaboration
Work with business stakeholders to frame problems suitable for AI and GenAI solutions.
Partner with Data Engineers to ensure clean, reliable, and scalable data inputs.
Collaborate with Analytics teams to validate outputs and ensure interpretability.
Contribute to AI best practices, reusable assets, and knowledge sharing across teams.
Required Qualifications
Bachelor s or Master s degree in Data Science, Computer Science, Engineering, or a related quantitative field.
6+ years of experience in data science, machine learning, or advanced analytics roles.
Strong proficiency in Python and common data science libraries.
Hands on experience building and deploying machine learning models in production environments.
Practical experience developing Generative AI or LLM based solutions.
Preferred Qualifications
Experience with RAG architectures, prompt engineering, embeddings, and agentic workflows.
Familiarity with enterprise GenAI platforms or AI studios.
Experience with model deployment, monitoring, and MLOps practices.
Exposure to regulated industries (healthcare, financial services) and responsible AI practices.
Cloud experience (Azure, AWS, or GCP) supporting ML or GenAI workloads.
AI or cloud certifications are a plus.
Key Skills & Competencies
Applied machine learning and statistics
Generative AI application development
Prompt engineering and LLM evaluation
Model deployment and lifecycle management
Responsible AI and governance awareness
Strong communication and stakeholder collaboration
Success Measures
AI and GenAI solutions move successfully from experimentation to production.