Lead Data Scientist – GenAI & Traditional AI

About NStarX

NStarX is an AI-first enterprise transformation company delivering “Service-as-Software” solutions through our unified DLNP (Data Lake and Neural Platform). We help enterprises become AI-native through federated learning, generative AI, data engineering, and platform services, with a strong focus on data sovereignty and privacy-preserving AI solutions. Our clients span healthcare, financial services, media, and other regulated industries.

Role Summary

We are looking for a Lead Data Scientist with deep, hands-on experience across Traditional AI/ML and Generative AI (GenAI). This role will lead end-to-end AI experimentation and delivery across multiple parallel initiatives, guide and mentor senior data scientists/engineers, and actively participate in client-facing activities (workshops, demos, solutioning). You will also contribute to delivery governance—defining scope, estimating effort, building sprint/resource plans, and ensuring execution quality.

Key Responsibilities

Technical Leadership & Delivery

• Lead AI/ML solution design and implementation across multiple projects running in parallel.
• Break down complex AI use cases into well-defined tasks and milestones; guide Senior Data Scientists and cross-functional teams to successful delivery.
• Own experimentation strategy: dataset readiness, feature engineering, model selection, tuning, evaluation, and iteration loops.
• Ensure production-grade readiness: performance, reliability, scalability, cost-efficiency, and monitoring/observability requirements.

GenAI / LLM Expertise

• Drive development of GenAI features using proprietary and open-source LLM ecosystems.
• Demonstrate strong understanding of GenAI architectures and underlying mathematics (e.g., transformer fundamentals, attention mechanisms, optimization, embeddings, decoding strategies, fine-tuning approaches).
• Build and optimize RAG pipelines (chunking strategies, embeddings, retrieval, reranking, grounding, evaluation).
• Design and implement agentic workflows using modern agent frameworks and tool integrations (planning, tool-use, multi-step execution, safety/guardrails).
• Establish evaluation frameworks for GenAI quality (hallucination risk, faithfulness, relevance, latency, cost).

Traditional AI / ML Expertise

• Lead classical ML initiatives including supervised/unsupervised learning, time series, NLP (non-LLM), recommendation, anomaly detection, etc., as applicable.
• Define end-to-end ML workflows: data pipelines, training, validation, deployment patterns, and performance tracking.

Stakeholder & Client Engagement

• Participate in client discussions: requirement discovery, solution walkthroughs, technical deep-dives, and demos.
• Translate business needs into implementable AI deliverables with clear success criteria.
• Provide regular status updates, risks, and mitigation plans to stakeholders.

Planning, Governance & Execution Management

• Own/drive SOW scope inputs and contribute to task-level resource planning and estimations.
• Create sprint plans, manage execution priorities, and coordinate dependencies across AI, engineering, and DevOps teams.
• Define best practices, reusable assets, and internal standards across experimentation and delivery.

Required Qualifications

• 10+ years of hands-on experience in AI/ML and data science experimentation with proven delivery outcomes.
• Demonstrated experience leading teams and guiding implementation of AI features end-to end.
• Strong experience working on multiple projects in parallel and handling competing priorities.
• Strong understanding of both proprietary and open-source model ecosystems and trade offs (cost, privacy, latency, deployment constraints).
• Hands-on experience with RAG and agentic frameworks (design + implementation).
• Ability to structure work into clear tasks, guide senior team members, and ensure high quality execution.
• Strong communication skills for client interactions, demos, and stakeholder alignment.

Preferred / Nice-to-Have

• Experience building enterprise-grade AI systems (security, governance, auditability, data privacy).
• Experience with LLM fine-tuning techniques (LoRA/QLoRA, instruction tuning, domain adaptation) and evaluation tooling.
• Experience with MLOps/LLMOps patterns (CI/CD, model monitoring, prompt/version management, A/B testing).
• Exposure to multi-cloud or hybrid deployments (AWS/Azure/on-prem).

Key Competencies

• Technical depth in both GenAI and Traditional ML
• Ownership mindset and delivery rigor
• Strong problem decomposition and team guidance
• High-quality stakeholder management and client communication
• Ability to balance experimentation speed with production readiness

Reporting & Collaboration

• Works closely with: AI Engineers, Data Engineers, MLOps/DevOps, Full-stack/Backend teams, Product/Program managers.
• Owns technical direction and delivery leadership for AI components across programs.

Why Join NStarX?

At NStarX, you’ll have the opportunity to shape how enterprises understand and adopt transformative AI technologies. Your content will directly influence how organizations think about data sovereignty, privacy-preserving AI, and enterprise transformation. You’ll work alongside talented technologists and business leaders who are pioneering the future of enterprise AI.

To Apply

Please submit your resume and a cover letter explaining your interest in this role to careers@nstarxinc.com.

Include “Lead Data Scientist – GenAI & Traditional AI” in the subject line.

NStarX is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.