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About This Role
Be part of something groundbreaking
At AIG, we are making long\-term investments in a brand\-new, innovative Generative AI team, designed to explore new possibilities for how artificial intelligence can be applied in insurance and beyond, and we need your help.
With the support and investment needed to explore new frontiers in generative AI, you’ll be working alongside talented colleagues, innovating and leading projects that will transform how we manage risk and serve our customers.
This team is central to our vision of the future and the core of our business offering. We will incorporate best\-in\-class engineering and product management principles and your guidance and collaboration will be critical to its success. To rapidly advance and innovate, we need your skills and expertise to build and scale world\-class products. If you’re excited by the opportunity to create meaningful impact, we’d love to hear from you.
Who we are
AIG is a leading global insurance organization providing a wide range of property casualty insurance and other financial services. We provide world\-class products and expertise to businesses and individuals in approximately 190 countries and jurisdictions.
At AIG, we’re reshaping how the world manages risk, and we’re inviting you to be a key part of that transformation.
How you will create impact
As aData Scientist at AIG, you will serve a critical role in the architecture, development, and delivery of impactful enterprise\-level Generative AI solutions. We are seeking a highly technical individual contributor with extensive hands\-on experience, as we are an applied data science team operating in an agile and production\-oriented environment. You will be focused on Large Language Models (LLMs) and Machine Learning (ML), acting as a primary technical driver for our most complex projects.
Your Responsibilities
- End\-to\-End Development: Lead the development and successful delivery of data science and generative AI solutions in accordance with business requirements.
- Production Oversight: Monitor solutions in production to ensure performance, reliability, and accuracy.
- Technical Collaboration: Partner with cross\-functional teams including product managers, engineers, and business leaders to translate requirements into technical reality.
- Evaluation \& Quality: Build robust evaluation frameworks to measure LLM efficacy, manage ground truth dataset quality, and guide the product development roadmap.
- Architecture: Design scalable ML pipelines and RAG frameworks that integrate seamlessly with enterprise data structures.
What is Needed to be Successful
- 8\+ years of Engineering Experience in a development environment
- 6\+ years of experience in a data science role, with a strong emphasis on NLP and ML, working in an agile production\-oriented environment.
- 3\+ years of practical experience with open\-source Large Language Models (Llama 3, Mixtral, etc.), including prompt engineering, inference optimization, and fine\-tuning.
- 3\+ years of experience building Generative AI Solutions, including designing and building RAG frameworks, validation pipelines, observability, and monitoring solutions.
- Proven Delivery: Experience successfully delivering multiple GenAI, analytical, or ML projects from conception through to production.
- Python Expertise: Strong, expert\-level proficiency in Python and its data science ecosystem (e.g., PyTorch, Pandas, Scikit\-learn).
- Education: Master’s degree in data science, Computer Science, or a related quantitative field.
Added Bonus / Preferred Qualifications
- Palantir Platform Experience: Hands\-on experience or certification in the Palantir platform (Foundry/AIP).
- Ontology Mastery: A strong understanding of Ontology—specifically how to map complex real\-world data entities and relationships into a digital twin framework to power AI applications.
- Large\-scale Data Infrastructure: Strong understanding of performance optimization in big data environments, with hands\-on experience using distributed data processing frameworks such as Apache Spark or PySpark.
- Agentic Solution: Experience implementing advanced agentic architectures, such as autonomous agents capable of multi\-step reasoning and decision\-making or integrating agentic solutions with large\-scale enterprise systems.
Required Competencies
- Ability to thrive in a fast\-paced, high\-stakes environment.
- Strong communication skills with the ability to explain complex technical concepts to non\-technical stakeholders.
- A "hands\-on" mindset with the desire to be a primary technical lead through code quality and architectural excellence.
Ready to set new industry standards? We would love to hear from you.
Veterans are encouraged to apply.
\#LI\-RG2 \#LI\-AIG \#AI \#GenAI \#artificialintelligence \#DataScience \#BigData
At AIG, we value in\-person collaboration as a vital part of our culture, which is why we ask our team members to be primarily in the office. This approach helps us work together effectively and create a supportive, connected environment for our team and clients alike.
Enjoy benefits that take care of what matters
At AIG, our people are our greatest asset. We know how important it is to protect and invest in what’s most important to you. That is why we created our Total Rewards Program, a comprehensive benefits package that extends beyond time spent at work to offer benefits focused on your health, wellbeing and financial security—as well as your professional development—to bring peace of mind to you and your family.
Reimagining insurance to make a bigger difference to the world
American International Group, Inc. (AIG) is a global leader in commercial and personal insurance solutions; we are one of the world’s most far\-reaching property casualty networks. It is an exciting time to join us — across our operations, we are thinking in new and innovative ways to deliver ever\-better solutions to our customers. At AIG, you can go further to support individuals, businesses, and communities, helping them to manage risk, respond to times of uncertainty and discover new potential. We invest in our largest asset, our people, through continuous learning and development, in a culture that celebrates everyone for who they are and what they want to become.
Welcome to a culture of inclusion
We’re committed to creating a culture that truly respects and celebrates each other’s talents, backgrounds, cultures, opinions and goals. We foster a culture of inclusion and belonging through learning, cultural awareness activities and Employee Resource Groups (ERGs). With global chapters, ERGs are a cornerstone for our culture of inclusion. The talent of our people is one of AIG’s greatest assets, and we are honored that our drive for positive change has been recognized by numerous recent awards and accreditations.
*AIG provides equal opportunity to all qualified individuals regardless of race, color, religion, age, gender, gender expression, national origin, veteran status, disability or any other legally protected categories.*
AIG is committed to working with and providing reasonable accommodations to job applicants and employees with disabilities. If you believe you need a reasonable accommodation, please send an email to [email protected].
Functional Area:
DT \- Data
AIG PC Global Services, Inc.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At AIG, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AIG AI Hiring
AIG has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Atlanta, GA, US, New York, NY, US, Jersey City, NJ, US. Compensation range: $280K - $280K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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