Data Scientist, GenAI

What matters most

• Own delivery from requirements to production

  The role is a hands-on individual contributor and primary technical driver for complex GenAI and data science projects. Prepare a concise account of how you have translated business requirements into architecture, code, delivery decisions and a production outcome.

• Make LLM quality measurable

  AIG calls for evaluation frameworks that measure LLM efficacy, maintain ground-truth dataset quality and inform the product roadmap. Prepare to explain how you would choose evaluation criteria, validate reference data and use results to prioritize improvements.

• Build enterprise-ready RAG and ML architecture

  The role designs scalable ML pipelines and RAG frameworks integrated with enterprise data structures. Prepare to explain architectural choices across retrieval, model inference, validation and integration, including why you would make each choice.

• Sustain production performance

  Production oversight covers performance, reliability and accuracy; the posting also calls for observability and monitoring experience. Prepare an example of how monitoring revealed a problem, how you investigated it and what changed afterward.

• Lead technically across functions

  The scientist partners with product managers, engineers and business leaders and must explain technical concepts to non-technical stakeholders. Prepare to show how you communicate trade-offs while remaining hands-on with code quality and architecture.

Likely challenges to think through

• Balancing LLM quality with inference demands

  A likely design challenge is improving answer quality without making an open-source LLM solution impractical to run. The posting calls for prompt engineering, inference optimization and fine-tuning; prepare to compare when each approach is worth its implementation and operating cost.

• Distinguishing retrieval failures from model failures

  In an enterprise RAG system, an inaccurate answer could stem from retrieved material, integration or generation. This is a preparation hypothesis, not a reported AIG problem. Prepare a diagnostic approach that tests retrieval and generated answers separately against maintained ground truth.

• Moving quickly without weakening production controls

  The agile, fast-paced setting and end-to-end delivery remit may create tension between iteration speed and reliable production behavior. Prepare to describe staged validation, monitoring and criteria for deciding whether a change is ready to release.

Topics to prepare

• Open-source LLM trade-offs

  The posting calls for open-source LLM experience, including prompt engineering, inference optimization and fine-tuning, and names Llama 3 and Mixtral as examples. To revise, compare when you would change a prompt, optimize inference or fine-tune a model, explaining the quality and performance trade-offs.

• RAG and enterprise data architecture

  The role designs scalable RAG frameworks and ML pipelines that integrate with enterprise data structures. Sketch the components of a RAG system, then explain how changes to source data could affect retrieval, answers and pipeline reliability.

• LLM evaluation and ground truth

  The posting explicitly calls for evaluation frameworks, ground truth dataset quality and validation pipelines. Draft an evaluation plan that defines answer-quality criteria, checks reference-data quality and explains how results would inform product decisions.

• Production oversight

  The role monitors production performance, reliability and accuracy, with observability and monitoring named in the qualifications. Work through a hypothetical decline in answer accuracy: distinguish possible data, retrieval and model causes, and decide what evidence to examine first.

• Ontologies and digital twins

  Preferred, not required: the posting mentions mapping real-world entities and relationships into a digital twin framework and lists Palantir Foundry/AIP experience or certification as a bonus. As optional preparation, diagram entities and relationships for a simple risk-management example and explain how that representation could support an AI application.

Skills to revise

• Python data science implementation

  Expert Python proficiency is required; PyTorch, Pandas and Scikit-learn are named examples. Practice cleaning a small text dataset with Pandas, building a simple ML baseline with Scikit-learn and explaining where PyTorch would fit if a model needed training or adaptation.

• Prompt and model comparison

  The posting requires practical open-source LLM work. On a small, fixed set of example questions, compare two prompt variants for a model such as Llama 3 or Mixtral; record answer quality and inference time, then justify which change you would try next. This is a suggested exercise, not a stated employer test.

• RAG validation prototype

  The role calls for building RAG frameworks and validation pipelines. Using a small, self-contained collection of documents, implement retrieval and answer generation, then add checks for missing source material and answers unsupported by the retrieved text. This prototype is preparation, not a described AIG project.

• Evaluation and monitoring workflow

  The role calls for ground truth management, LLM evaluation and production monitoring. Create a versioned set of test questions and reference answers, run a repeatable evaluation, and specify what changes in accuracy, reliability or performance would prompt investigation.

• Technical delivery communication

  The posting expects an individual contributor to lead complex work from business requirements through production and explain technical concepts to non-technical stakeholders. Practice turning a hypothetical risk-management request into a brief technical plan with success measures, architecture choices and a plain-language explanation of trade-offs.

• Map the stated requirements to evidence

  The posting specifies production-oriented NLP/ML experience, multiple delivered projects, Python expertise, open-source LLM work, GenAI solutions and a relevant master’s degree. Prepare a brief evidence table with dates, your contribution and verifiable outcomes; leave gaps unclaimed.

• Build an end-to-end project walkthrough

  The role leads solutions from business requirements through production. Prepare one walkthrough showing the problem, your hands-on technical decisions, collaboration with product and engineering, delivery and measured results.

• Practice a scalable RAG design exercise

  The posting calls for ML pipelines and RAG frameworks integrated with enterprise data structures. Sketch a hypothetical insurance or risk-management use case, label it as hypothetical, and explain retrieval, data integration, validation and scaling trade-offs.

• Explain your open-source LLM decisions

  The posting names models such as Llama 3 and Mixtral, plus prompting, inference optimization and fine-tuning. Prepare a comparison of approaches you have actually used, with code or experiment evidence where available; do not imply experience you cannot substantiate.

• Draft an evaluation and production-monitoring plan

  The role measures LLM efficacy, manages ground-truth quality and monitors accuracy, reliability and performance. Prepare a sample evaluation rubric, dataset-quality checks, monitoring signals and a way findings could inform product decisions.

• Rehearse a non-technical explanation

  The posting requires communication with business leaders and hands-on technical leadership through code quality and architecture. Practice a short account of a technical trade-off, its business impact and how you worked with cross-functional partners.

• Prepare targeted questions for the team

  Ask how the new GenAI team chooses use cases, defines production success and owns ground-truth datasets. You can also clarify expectations for primarily in-office work and whether the preferred Foundry/AIP and ontology experience is relevant to this role’s immediate projects.