Lead Data Scientist Applied AI

The Role

We are seeking a Lead Data Scientist who can build advanced AI systems and demonstrate, with evidence, why they are accurate, reliable and appropriate for production. This role combines strong statistical and machine learning expertise with hands‑on experience designing and delivering production AI solutions.

You will work across Generative AI, Agentic AI, computer vision, forecasting and optimization. Your central responsibility will be to measure model performance and uncertainty, explain model behavior, identify risks and failure modes, and connect technical results to business outcomes that leaders can use to make decisions.

The ideal candidate is comfortable running structured experiments, presenting error analysis to technical and non‑technical stakeholders, and taking models from problem definition through deployment and monitoring. You should know when Generative AI is the right solution, when a simpler statistical approach is more effective, and how to support that decision with data.

• Justify modeling decisions with evidence
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  Frame business problems, form hypotheses, run structured experiments and select between classical machine learning, deep learning and Generative AI using measured performance, cost and risk.
• Quantify risk and uncertainty
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  Measure confidence intervals, error rates, hallucination rates, bias, drift and failure modes. Define numerical production‑readiness criteria for each use case.
• Improve explainability
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  Use feature attribution, SHAP, LIME, error analysis, slice analysis and evaluation dashboards to explain model behavior and limitations.
• Translate results into business impact
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  Connect model metrics to revenue, cost, speed and risk. Quantify expected return, trade‑offs and downside scenarios for decision‑makers.

• Design complete AI solutions
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  Own problem framing, data strategy, modeling, evaluation, deployment and monitoring for machine learning, LLM and agentic systems.
• Build models for unstructured data
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  Develop production‑quality solutions using image, video, text, audio, sensor and time‑series data.
• Deliver computer vision solutions
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  Build detection, classification, segmentation, OCR and tracking systems with measurable performance benchmarks.
• Develop forecasting solutions
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  Create time‑series, demand and behavioral forecasting models with documented accuracy, error bands and integration into operational workflows.
• Build with foundation models
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  Use Claude and other LLMs for prompting, fine‑tuning, systematic evaluation and integration into multi‑step, tool‑using and multi‑agent workflows with memory and guardrails.

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics or a related discipline, or equivalent practical experience.
• A track record of approximately 10 or more AI and machine learning projects deployed to production, with clear ownership of approach selection, evaluation and risk assessment.
• Strong statistical and algorithmic foundations, including hypothesis testing, experiment design, evaluation methodology and uncertainty quantification.
• Hands‑on experience with RAG, tool‑use patterns and agentic frameworks such as Lang Graph, Llama Index, Auto Gen or CrewAI, along with Model Context Protocol where relevant.
• Experience evaluating and protecting LLM and agentic systems through guardrails, systematic testing, hallucination measurement and failure analysis.
• Practical experience with explainability, bias and fairness assessment, model monitoring and drift detection.
• Familiarity with MLOps practices, including experiment tracking, CI/CD for machine learning, model registries and production monitoring.
• Exper…