Manager Data Science, GenAI

What matters most

• Deliver business-aligned GenAI solutions

  The manager oversees enterprise-level solutions aligned with business requirements and creates technical plans to ship them on time in an agile, production-oriented environment.

• Remain hands-on technically

  The posting explicitly calls for a practicing manager, not a purely managerial or architecture-focused role. It names hands-on work with prompts, RAG, agentic frameworks and validation pipelines.

• Make evaluation guide product decisions

  A stated responsibility is building frameworks that measure LLM effectiveness and ground-truth dataset quality, then using the results to inform the product roadmap.

• Own production monitoring

  The role includes monitoring delivered solutions in production; the qualifications also call for experience with observability and monitoring solutions.

• Develop the team and coordinate delivery

  The manager builds and oversees a data science team, mentors junior data scientists, and works with product managers, engineers and business leaders.

Likely challenges to think through

• Balancing technical depth with team leadership

  Likely preparation challenge: hands-on development can compete for time with mentoring and delivery oversight. Prepare an example that distinguishes technical decisions you owned from work you delegated and explains how you supported the team.

• Choosing what evaluation results mean

  Likely preparation challenge: an LLM score may be hard to interpret if the ground-truth dataset is weak. Prepare to explain how you would assess dataset quality, interpret effectiveness results and decide what should change on the roadmap.

• Shipping promptly without losing production visibility

  Likely preparation challenge: delivery deadlines can pull against validation and monitoring work. Prepare a concrete account of how you would plan releases, define validation checks and use production observations to guide follow-up work.

Topics to prepare

• Prompting, RAG and agentic design

  The posting requires hands-on generative AI experience with prompt engineering, retrieval-augmented generation (RAG) and agentic frameworks. As a revision exercise, sketch how each approach would handle the same hypothetical insurance-information question, including when retrieval or multi-step actions would add value.

• LLM and ground-truth evaluation

  The role builds frameworks to assess LLM effectiveness and ground-truth dataset quality. Draft a conceptual evaluation rubric for a hypothetical risk-management answer, then identify how inaccurate or ambiguous reference answers could distort the results.

• Validation and production monitoring

  Validation pipelines, observability and monitoring are stated requirements, and the manager monitors deployed solutions. Trace a hypothetical release from pre-release checks through production monitoring; explain what evidence would prompt investigation.

• Technical delivery and team leadership

  The manager must plan and ship work, mentor junior data scientists, and collaborate with product managers, engineers and business leaders while remaining hands-on. For revision, outline the decisions and team responsibilities in a hypothetical delivery plan, including how business requirements become testable milestones.

• Ontologies and digital twins — preferred

  The posting lists mapping real-world entities and relationships into a digital-twin framework as a preferred qualification. As optional preparation, draw a small conceptual entity-and-relationship model for a hypothetical insurance use case and explain how it could support an AI application.

• Distributed-data performance — preferred

  Big-data optimization using distributed processing such as Apache Spark or PySpark is preferred, not required. As optional preparation, review a hypothetical slow distributed-data workflow and explain where data volume, partitioning or joins might affect performance.

Skills to revise

• Prototype and compare GenAI approaches

  As a practice exercise, build a small hypothetical question-answering flow with a prompt-only version and a RAG version, then describe how an agentic version would differ. This rehearses the posting’s explicit hands-on prompt, RAG and agentic-framework requirement; no particular framework is specified.

• Construct an evaluation set and score outputs

  Create a small, clearly labeled practice set of questions and reference answers; check the references for ambiguity, score sample LLM outputs, and summarize failure patterns. This directly rehearses the required LLM and ground-truth evaluation work.

• Implement validation and monitoring checks

  For a practice GenAI flow, define input and output validation checks, record observable failure cases, and make a simple production-monitoring checklist. The exercise reflects the posting’s validation-pipeline, observability and monitoring requirements; it is not a claim about AIG’s tooling.

• Write and communicate a delivery plan

  Turn a hypothetical business request into scoped milestones, evaluation criteria, engineering dependencies and a mentoring task; practice explaining trade-offs to product, engineering and business stakeholders. This mirrors the role’s delivery, collaboration and people-management responsibilities.

• Profile a distributed-data job — preferred

  If revising the preferred big-data qualification, use Apache Spark or PySpark to inspect a practice data-processing job, identify a bottleneck and compare performance before and after a change. The posting names these frameworks as examples, not mandatory tools.

• Map the stated requirements to evidence

  Prepare a one-page evidence map for the posted experience and degree requirements: 7+ years in data science, 3+ years managing data scientists or engineers, 2+ years of hands-on GenAI development, and a relevant master’s or Ph.D. Note where NLP or production-oriented work applies; do not fill gaps with assumptions.

• Build a hands-on GenAI project walkthrough

  The role emphasizes hands-on delivery, not management or architecture alone. Prepare one project walkthrough showing your own technical contributions to prompt engineering, RAG or agentic frameworks, validation pipelines, and production observability or monitoring, where applicable.

• Draft an LLM evaluation example

  The posting calls for frameworks that assess LLM effectiveness and ground-truth dataset quality. Prepare a concise example with proposed measures, dataset-quality checks, and how results would inform a product roadmap; label any example not drawn from your work as hypothetical.

• Practice a technical delivery plan

  For a hypothetical enterprise GenAI use case in insurance or risk management, outline business requirements, implementation milestones, cross-team dependencies, production monitoring, and how you would manage delivery timing. This is a preparation exercise, not a stated AIG project.

• Select people-management and collaboration examples

  Prepare brief examples of mentoring junior data scientists, managing a team, and working with product managers, engineers, and business leaders. Identify your actions, decisions, and outcomes without implying experience you have not had.

• Prepare optional depth on preferred qualifications

  If relevant to your experience, assemble specific examples of Palantir work or certification, ontology-to-digital-twin mapping, Spark or PySpark performance optimization, or multi-step agents and enterprise integration. The posting identifies these as preferred, not required.

• Write targeted questions for the employer

  Ask how the new GenAI team selects use cases, measures LLM and ground-truth quality, divides hands-on work from team management, and monitors deployed solutions. You can also clarify what primarily in-office work means for this Atlanta role.