Lead Data Scientist

Responsibilities

- Leading the design and development of advanced data solutions to transform raw data into actionable insights
- Guiding teams in leveraging advanced analytics and statistical techniques for data-driven decision making
- Utilizing machine learning and artificial intelligence to enhance data science workflows and predictive modeling
- Developing and implementing data pipelines and data lakes to support complex data analysis
- Overseeing the creation of data visualizations to solve complex business problems and inform strategic decisions
- Collaborating with client teams to identify opportunities for data-driven growth and innovation
- Mentoring team members to develop their skills in data science and analytics
- Validating data quality and integrity within analytics frameworks
- Encouraging the adoption of innovative technologies and leading practices in data engineering
- Addressing and resolving conflicts or issues with clients and team members to maintain project timelines and deliverables

What You Must Have

- At least a Bachelor's & Master's degree
- At least 8 years of experience
- Oral and written proficiency in English required

What Sets You Apart

- Over 3 years of experience in developing and scaling Generative AI projects from prototypes to enterprise production, managing throughput, latency, cost, and multi-region deployments.

- Proven expertise implementing AI interoperability protocols like MCP (Model Context Protocol) and A2A (Agent-to Agent) at scale for seamless system integration.

- Skilled in using enterprise cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, and Google Vertex AI to build and deploy production-grade agentic AI solutions.

- Advanced Python programming skills and hands-on experience with agentic AI frameworks including LangChain, LangGraph, CrewAI, and AutoGen for building robust generative AI applications.

- Deep understanding of advanced Retrieval

- Augmented Generation (RAG) architectures (Graph RAG, Vectorless RAG, Hybrid RAG) and traditional AI/ML fundamentals like model building, fine-tuning, and evaluation.

- Strong knowledge of LLM security risksprompt injection, jailbreaking, data exfiltration, tool misuse—and experience designing defense-in-depth safeguards within agentic system architectures.

- Expertise in containerization and cloud-native orchestration (Kubernetes, Docker, serverless) and event-driven architectures for scalable deployment of agentic AI workloads; holds relevant AI or solution architecture certifications.