Lead Data Scientist

Responsibilities

• Apply LLMs and agentic systems pragmatically, and drive adoption of evolving capabilities. Continuously evaluate new models, tooling and patterns; run fast, measurable prototypes; and scale the winners into production RAG/agent workflows i.e., owning evaluation, guardrails and hallucination control
• Build and own end-to-end production systems. delivering robust, observable services that run reliably at scale i.e., from data cleaning and feature engineering through model development, evaluation, deployment, monitoring, retraining and incident

Requirements

• This role is designed for a hands-on Data Science leader who combines strong fundamentals with strong engineering instincts. You'll likely recognize yourself in many of the following:
• Strong judgment. You balance rigor, speed and maintainability and can explain trade-offs in a way that helps teams and stakeholders make good decisions
• Hands-on, by choice. You write production-quality Python code, review code thoughtfully and treat coding as a core part of how you build and think
• Systems builder. You've taken solutions from a blank repo to production systems that real users or business processes depend on
• Strong in fundamentals. You can explain why methods work, when they break and what assumptions they rely on in clear, simple language. Linear algebra, probability, optimization and statistical inference are tools you use actively
• Comfortable with ambiguity. You can take a vague problem, clarify goals and constraints, and turn it into a well-defined plan before selecting the approach
• 8+ years in Applied ML / Applied AI (including LLM & GenAI), with 6+ years hands-on experience building and deploying models in production environments
• Modern LLM stack: RAG, agentic workflows, evaluation frameworks, prompt engineering, fine-tuning trade-offs, and vector databases
• Deep learning fundamentals: understanding when to apply deep learning approaches and how to train and evaluate them effectively in practice
• NLP fundamentals: text preprocessing, embeddings, similarity/search, topic modeling, classification, and evaluation
• Causal ML: experience with at least one: DML, uplift modeling, IV, propensity scoring, synthetic control, or difference-in-differences, applied in a decision-making or production context
• Optimization: linear/integer programming, constrained optimization, bandits, or RL applied to real-world problems
• Expert-level Python. Comfortable with the scientific stack (NumPy, pandas, scikit-learn, PyTorch) and with writing clean, tested, modular code
• Working knowledge of Agentic AI Frameworks like Langchain, Langgraph and Deepagents or equivalent
• Cloud Computing (AWS / Azure / GCP) - model training, deployment, scaling, cost-awareness

Benefits

Additional Information

About this role We are looking for a Lead Data Scientist comfortable framing an ambiguous business problem, someone with broad methodological range and the flexibility to choose the right approach for each problem. The problems we work on cut across supervised and unsupervised learning, causal inference, optimization, recommendation, forecasting, and increasingly LLM-based systems including Agentic AI. Candidate should be able to use the right tool for the problem while keeping the approach driven by the business question & hence lead by example. This is a hands-on (~70% IC work involving modeling, coding, system design and ~30% mentorship and stakeholder engagement.

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Company Intel

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