02 // TAXONOMY & ROLE PROFILES

AI & Data Role Analysis (21 Canonical Profiles)

A practical breakdown of 21 data and AI roles based on active job openings across S&P 500 enterprises. See the core skills, engineering tools, and company types each role actually requires.

Top 12 AI & Data Roles by Active Job Postings

Source: 508 S&P 500 corporations + frontier tech leaders and European market

CANONICAL ROLES

Top 12 AI & Data Roles by Active Job Postings : Source: 508 S&P 500 corporations + frontier tech leaders and European market
Metric / Technology	Active Positions
AI Developer	1529
Data Engineer	988
Data Analyst	977
Machine Learning Engineer	912
Data Scientist	863
Research Scientist (AI/ML)	380
AI / Data Product Manager	191
Data Architect	171
BI / Analytics Manager	101
Engineering Manager (AI/Data)	94
Director / VP of Data & AI	93
Analytics Engineer	92

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AI/MLICCZ: 68 positions

AI Developer

1,529 positions

Share: 25.38% • Senior+: 63.8%

Installed base: 1,541(99.2%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The AI Developer is the fastest-growing engineering profile across S&P 500 enterprises, positioned directly at the crossroads of software engineering and applied AI. Rather than training models from scratch, these engineers integrate Large Language Models (LLMs), build agentic workflows, and deploy production-grade RAG pipelines into products.

Companies look for solid software engineering skills in Python or TypeScript paired with the discipline to manage probabilistic systems: from automated evaluations (Evals) and guardrails to latency and cost control across AWS, GCP Vertex AI, and Azure.

• Required Core Skills & Domain

Python41.6%
AI Agents / Agentic AI36%
LLM / Large Language Models33.4%
GenAI / Generative AI23.3%
RAG (Retrieval-Augmented Generation)17.5%
SQL12.2%
Java9.5%
Fine-Tuning9.3%

• Technology Stack & Tooling

MLOps / Evals & Observability16.4%
AWS15.8%
Google Cloud Platform15.5%
Microsoft Azure15.5%
PyTorch11.7%
Kubernetes10.4%
Docker9.4%
LangChain8%

• Top Hiring Companies

OpenAI103 positions
UnitedHealth Group88 positions
Alphabet Inc. (Class C)61 positions
Anthropic61 positions
Google (Alphabet)61 positions
Alphabet Inc. (Class A)61 positions

• Top S&P 500 Sectors (Industry)

Information Technology303 (27%)
Communication Services204 (18.2%)
Health Care178 (15.9%)
Financials173 (15.4%)
Industrials120 (10.7%)

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Data EngineeringICCZ: 47 positions

Data Engineer

988 positions

Share: 16.4% • Senior+: 58.8%

Installed base: 46,799(2.1%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Data Engineer builds and maintains the data foundations required by modern analytics and AI systems. Without dependable data ingestion, clean transformation pipelines, and rigorous observability, reporting and machine learning projects stall. This keeps Data Engineering among the most consistently recruited technical tracks.

Modern data engineering applies software best practices: writing clean Python and SQL, designing cloud data warehouses and lakehouses (BigQuery, Snowflake, Databricks), and adopting table formats like Apache Iceberg and Delta Lake. Orchestration tools (Airflow, Dagster), version control, and data contract enforcement are core requirements.

• Required Core Skills & Domain

Python31.3%
SQL29.4%
Data Governance & Quality24.1%
AI Agents / Agentic AI12.3%
Java12.1%
LLM / Large Language Models8.4%
GenAI / Generative AI8.1%
Scala6.6%

• Technology Stack & Tooling

Apache Spark21.9%
AWS18.7%
Databricks16.3%
Google Cloud Platform13.6%
Microsoft Azure13.5%
Snowflake11.4%
Apache Airflow8.2%
dbt7.8%

• Top Hiring Companies

Alphabet Inc. (Class C)62 positions
Google (Alphabet)62 positions
Alphabet Inc. (Class A)62 positions
UnitedHealth Group42 positions
Amazon22 positions
Databricks21 positions

• Top S&P 500 Sectors (Industry)

Communication Services222 (27.4%)
Financials145 (17.9%)
Health Care114 (14.1%)
Information Technology112 (13.8%)
Consumer Discretionary91 (11.2%)

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Analytics & DSICCZ: 20 positions

Data Analyst

977 positions

Share: 16.22% • Senior+: 37.8%

Installed base: 51,483(1.9%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Data Analyst remains central to helping organizations make decisions grounded in solid evidence. In modern data stacks, the focus has moved from ad-hoc spreadsheets to deep business domain knowledge, clean metric definition, and working with governed semantic layers.

Core expectations include advanced SQL and dimensional modeling, visualization platforms (Tableau, Power BI, Looker), and Python for exploratory analysis. High-impact analysts translate data in cloud warehouses (Snowflake, BigQuery, Databricks) into clear business direction.

• Required Core Skills & Domain

SQL18.6%
Excel13.8%
Python13.4%
Power BI10.5%
Data Governance & Quality10.1%
Tableau9.1%
R6.8%
GenAI / Generative AI2.7%

• Technology Stack & Tooling

Microsoft Azure3.1%
Snowflake2.5%
Apache Spark2.4%
Databricks1.9%
Google Cloud Platform1.9%
BigQuery1.4%
AWS1.2%
Apache Airflow1%

• Top Hiring Companies

TE Connectivity142 positions
Kinder Morgan36 positions
Arthur J. Gallagher & Co.31 positions
Schlumberger24 positions
Intercontinental Exchange22 positions
Mettler Toledo15 positions

• Top S&P 500 Sectors (Industry)

Information Technology196 (21%)
Financials184 (19.7%)
Industrials127 (13.6%)
Health Care95 (10.2%)
Energy72 (7.7%)

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AI/MLICCZ: 15 positions

Machine Learning Engineer

912 positions

Share: 15.14% • Senior+: 70%

Installed base: 9,867(9.2%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Machine Learning Engineer takes experimental models out of notebooks and puts them into reliable, production-ready services. Job postings emphasize end-to-end ownership of the ML lifecycle: building feature pipelines, distributed training, fine-tuning, and low-latency inference at scale.

The technical stack centers on Python, PyTorch, and MLOps fundamentals like Docker, Kubernetes, MLflow, and live model monitoring. Classical machine learning methods (recommenders, risk models) are now frequently combined with LLM serving in cloud environments.

• Required Core Skills & Domain

Python53.2%
LLM / Large Language Models30%
AI Agents / Agentic AI24.8%
GenAI / Generative AI19.5%
RAG (Retrieval-Augmented Generation)13.8%
Fine-Tuning13.3%
Java11.6%
Excel11.3%

• Technology Stack & Tooling

AWS23.5%
MLOps / Evals & Observability23%
Google Cloud Platform22.4%
Apache Spark19.7%
PyTorch19.4%
Microsoft Azure19.2%
TensorFlow14%
Databricks13.9%

• Top Hiring Companies

Databricks96 positions
UnitedHealth Group67 positions
Google (Alphabet)39 positions
Alphabet Inc. (Class C)39 positions
Alphabet Inc. (Class A)39 positions
OpenAI31 positions

• Top S&P 500 Sectors (Industry)

Communication Services160 (28.3%)
Information Technology141 (24.9%)
Health Care105 (18.6%)
Consumer Discretionary73 (12.9%)
Financials48 (8.5%)

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Analytics & DSICCZ: 22 positions

Data Scientist

863 positions

Share: 14.32% • Senior+: 58.4%

Installed base: 29,182(3%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Data Scientist role has matured significantly: organizations now prioritize specialists in causal inference, rigorous experimentation (A/B testing), optimization algorithms, and predictive modeling tied to revenue and risk reduction over generic data exploration.

Beyond fluency in Python, SQL, and core statistical libraries like scikit-learn and PyTorch, business acumen is essential. Senior data scientists must frame open business problems as mathematical tasks, validate assumptions, and work with engineers to deploy solutions.

• Required Core Skills & Domain

Python59%
SQL52.8%
R33.5%
GenAI / Generative AI18.2%
LLM / Large Language Models13.8%
AI Agents / Agentic AI11.4%
Natural Language Processing8.9%
Data Governance & Quality8.8%

• Technology Stack & Tooling

Apache Spark10.9%
MLOps / Evals & Observability8.8%
PyTorch7.6%
Databricks7.2%
AWS7.2%
scikit-learn6.5%
TensorFlow6.5%
Microsoft Azure6.3%

• Top Hiring Companies

Google (Alphabet)58 positions
Alphabet Inc. (Class C)58 positions
Alphabet Inc. (Class A)58 positions
Amazon26 positions
Amgen22 positions
Leidos21 positions

• Top S&P 500 Sectors (Industry)

Communication Services201 (28.4%)
Financials109 (15.4%)
Consumer Discretionary90 (12.7%)
Information Technology88 (12.4%)
Health Care78 (11%)

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AI/MLIC

Research Scientist (AI/ML)

380 positions

Share: 6.31% • Senior+: 40.3%

Installed base: 17,181(2.2%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Research Scientist in AI/ML focuses on advancing core algorithmic frontiers: designing novel neural architectures, multimodal systems, reinforcement learning methods, and post-training alignment techniques.

The profile typically requires a Ph.D. in Computer Science or Mathematics, publications at top-tier conferences (NeurIPS, ICML, ICLR, CVPR), and the ability to turn theoretical breakthroughs into scalable code using PyTorch or JAX across distributed GPU clusters.

• Required Core Skills & Domain

Python37.4%
LLM / Large Language Models34.7%
AI Agents / Agentic AI22.4%
Fine-Tuning21.8%
GenAI / Generative AI16.1%
R8.7%
Natural Language Processing6.3%
Computer Vision5.8%

• Technology Stack & Tooling

PyTorch23.7%
JAX14.7%
TensorFlow10%
Kubernetes4.7%
MLOps / Evals & Observability3.9%
pandas3.7%
scikit-learn3.2%
Hugging Face Transformers3.2%

• Top Hiring Companies

Alphabet Inc. (Class A)44 positions
Alphabet Inc. (Class C)44 positions
Google (Alphabet)44 positions
Anthropic43 positions
Amazon17 positions
OpenAI17 positions

• Top S&P 500 Sectors (Industry)

Communication Services135 (52.9%)
Information Technology47 (18.4%)
Consumer Discretionary22 (8.6%)
Industrials16 (6.3%)
Health Care16 (6.3%)

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ProductICCZ: 1 positions

AI / Data Product Manager

191 positions

Share: 3.17% • Senior+: 98.4%

Installed base: 1,989(9.6%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The AI Product Manager guides the strategy and delivery of products powered by machine learning and generative models. Unlike traditional software PMs, they must manage probabilistic behavior, handle output ambiguity, and balance inference costs with product value.

Strong candidates understand model capabilities (LLMs, multimodal architectures, RAG), establish clear evaluation frameworks (Evals, precision, latency), and align technical teams with genuine customer problems.

• Required Core Skills & Domain

AI Agents / Agentic AI35.1%
Data Governance & Quality21.5%
GenAI / Generative AI16.2%
SQL12.6%
LLM / Large Language Models12.6%
Python7.3%
Excel6.8%
RAG (Retrieval-Augmented Generation)3.1%

• Technology Stack & Tooling

MLOps / Evals & Observability8.9%
Microsoft Azure8.9%
Databricks6.8%
AWS5.8%
Google Cloud Platform5.2%
Snowflake3.7%
BigQuery3.7%
LangChain1.6%

• Top Hiring Companies

Veeva Systems18 positions
Databricks8 positions
Scale AI8 positions
Workday, Inc.7 positions
Datadog7 positions
MongoDB5 positions

• Top S&P 500 Sectors (Industry)

Information Technology50 (33.3%)
Health Care29 (19.3%)
Communication Services19 (12.7%)
Financials19 (12.7%)
Industrials12 (8%)

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Data EngineeringICCZ: 1 positions

Data Architect

171 positions

Share: 2.84% • Senior+: 100%

Installed base: 3,610(4.7%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Data Architect designs the structural blueprint for enterprise data: defining how information flows from operational systems through analytical tiers to end consumers. They establish standards for data modeling, platform selection, security, and governance across the company.

The position requires deep familiarity with modern architectural patterns: distributed lakehouses (Databricks, Apache Iceberg), cloud warehouses (BigQuery, Snowflake), and data contracts. An effective architect explains technical trade-offs clearly to both engineering leads and executive boards.

• Required Core Skills & Domain

Python46.2%
LLM / Large Language Models35.7%
SQL26.3%
AI Agents / Agentic AI25.7%
Excel19.9%
Data Governance & Quality17%
GenAI / Generative AI16.4%
RAG (Retrieval-Augmented Generation)10.5%

• Technology Stack & Tooling

Microsoft Azure32.7%
AWS32.2%
Databricks29.8%
MLOps / Evals & Observability27.5%
Google Cloud Platform26.3%
Apache Spark15.2%
Snowflake15.2%
Hugging Face Transformers6.4%

• Top Hiring Companies

Databricks34 positions
Anthropic27 positions
OpenAI21 positions
Teledyne Technologies4 positions
Stripe4 positions
Nvidia4 positions

• Top S&P 500 Sectors (Industry)

Information Technology17 (22.1%)
Financials15 (19.5%)
Health Care13 (16.9%)
Industrials11 (14.3%)
Consumer Staples7 (9.1%)

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ManagementManagement

BI / Analytics Manager

101 positions

Share: 1.68% • Senior+: 100%

Installed base: 5,228(1.9%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The BI & Analytics Manager leads teams of data analysts and BI developers, ensuring analytics deliverables directly address business priorities while mentoring team members in technical craft and career growth.

The position balances technical depth (SQL, BI platforms, dimensional modeling) with people leadership: managing stakeholder demands, maintaining metric consistency, and prioritizing delivery pipelines.

• Required Core Skills & Domain

SQL30.7%
Python21.8%
Tableau19.8%
Data Governance & Quality19.8%
Power BI16.8%
Excel11.9%
R10.9%
GenAI / Generative AI6.9%

• Technology Stack & Tooling

Databricks7.9%
AWS6.9%
BigQuery6.9%
Apache Spark4%
Microsoft Azure3%
Amazon Redshift2%
MLOps / Evals & Observability2%
dbt2%

• Top Hiring Companies

Marriott International6 positions
eBay Inc.5 positions
Abbott Laboratories5 positions
Amgen5 positions
Fox Corporation4 positions
Mettler Toledo3 positions

• Top S&P 500 Sectors (Industry)

Consumer Discretionary26 (26%)
Health Care21 (21%)
Communication Services14 (14%)
Industrials10 (10%)
Consumer Staples8 (8%)

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ManagementManagement

Engineering Manager (AI/Data)

94 positions

Share: 1.56% • Senior+: 100%

Installed base: 1,758(5.3%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Engineering Manager (AI & Data) leads engineering teams across data pipelines, MLOps, and applied AI. They oversee system reliability, code quality, and project delivery while unblocking engineers and supporting their career progression.

Effective managers combine prior engineering experience in distributed systems or ML with strong organizational skills, technical standard setting, and close alignment with product leadership.

• Required Core Skills & Domain

AI Agents / Agentic AI25.5%
LLM / Large Language Models22.3%
Python20.2%
GenAI / Generative AI19.1%
Data Governance & Quality13.8%
SQL13.8%
RAG (Retrieval-Augmented Generation)10.6%
Java9.6%

• Technology Stack & Tooling

MLOps / Evals & Observability18.1%
Apache Spark17%
Microsoft Azure14.9%
AWS13.8%
Databricks12.8%
Google Cloud Platform10.6%
Snowflake8.5%
Kubernetes7.4%

• Top Hiring Companies

UnitedHealth Group17 positions
McDonald's6 positions
Google (Alphabet)4 positions
Alphabet Inc. (Class A)4 positions
Alphabet Inc. (Class C)4 positions
Coinbase4 positions

• Top S&P 500 Sectors (Industry)

Health Care19 (27.5%)
Communication Services16 (23.2%)
Consumer Discretionary12 (17.4%)
Financials9 (13%)
Information Technology7 (10.1%)

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ExecutiveManagement

Director / VP of Data & AI

93 positions

Share: 1.54% • Senior+: 100%

Installed base: 1,250(7.4%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Director / VP of Data & AI leads the broader data and machine learning organization, ensuring technical investments translate into measurable business outcomes, managing cloud infrastructure budgets, and fostering data-informed decision-making.

Core skills involve bridging technology with executive priorities: articulating architectural trade-offs in financial and operational terms, hiring senior engineering leaders, and maintaining rigorous governance.

• Required Core Skills & Domain

Data Governance & Quality20.4%
GenAI / Generative AI15.1%
AI Agents / Agentic AI14%
Python14%
R11.8%
LLM / Large Language Models10.8%
SQL10.8%
RAG (Retrieval-Augmented Generation)6.5%

• Technology Stack & Tooling

MLOps / Evals & Observability9.7%
AWS7.5%
Apache Spark7.5%
Databricks6.5%
Snowflake6.5%
Microsoft Azure4.3%
Google Cloud Platform2.2%
Kubernetes2.2%

• Top Hiring Companies

UnitedHealth Group30 positions
Johnson & Johnson7 positions
Citigroup4 positions
TE Connectivity3 positions
Marriott International3 positions
Global Payments3 positions

• Top S&P 500 Sectors (Industry)

Health Care47 (50.5%)
Financials18 (19.4%)
Consumer Discretionary13 (14%)
Information Technology8 (8.6%)
Consumer Staples3 (3.2%)

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Data EngineeringICCZ: 7 positions

Analytics Engineer

92 positions

Share: 1.53% • Senior+: 38%

Installed base: 1,485(6.2%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Analytics Engineer bridges raw data infrastructure and downstream business analytics. They bring software engineering practices, such as Git version control, automated testing, documentation, and CI/CD pipelines, directly into data modeling and semantic layer design.

The role relies on advanced SQL alongside transformation frameworks like dbt and Google Cloud Dataform (SQLX). Analytics Engineers produce clean, tested, and well-documented datasets so analysts and leaders can rely on trusted business metrics.

• Required Core Skills & Domain

SQL37%
Python31.5%
Data Governance & Quality22.8%
LLM / Large Language Models13%
AI Agents / Agentic AI13%
Tableau10.9%
Power BI8.7%
Excel6.5%

• Technology Stack & Tooling

dbt19.6%
Snowflake16.3%
Databricks13%
Apache Airflow9.8%
AWS8.7%
Microsoft Azure6.5%
Apache Spark6.5%
Docker4.3%

• Top Hiring Companies

Netflix7 positions
Delivery Hero4 positions
Booking Holdings4 positions
Autodesk2 positions
ABOUT YOU2 positions
voize2 positions

• Top S&P 500 Sectors (Industry)

Communication Services11 (26.8%)
Information Technology7 (17.1%)
Financials7 (17.1%)
Consumer Discretionary7 (17.1%)
Health Care4 (9.8%)

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Analytics & DSICCZ: 6 positions

BI Developer / Analyst

77 positions

Share: 1.28% • Senior+: 35.1%

Installed base: 7,790(1%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The BI Developer designs, builds, and maintains interactive dashboards, analytical reporting systems, and data models for business users. The role focuses on delivering clear, fast, and methodologically accurate insights.

Core skills include strong SQL, dimensional modeling (star schemas), and enterprise visualization platforms (Power BI, Tableau, Looker). Success depends on understanding business goals and presenting data so stakeholders can act on it quickly.

• Required Core Skills & Domain

SQL32.5%
Power BI26%
Tableau20.8%
Python20.8%
Data Governance & Quality19.5%
Excel13%
Looker9.1%
AI Agents / Agentic AI3.9%

• Technology Stack & Tooling

Snowflake7.8%
BigQuery5.2%
AWS5.2%
Databricks3.9%
Microsoft Fabric3.9%
Amazon Redshift2.6%
Microsoft Azure2.6%
Apache Spark2.6%

• Top Hiring Companies

TE Connectivity10 positions
Genuine Parts Company3 positions
Dexcom3 positions
Snap-on2 positions
GSF Car Parts2 positions
Johnson Controls2 positions

• Top S&P 500 Sectors (Industry)

Information Technology18 (36%)
Industrials11 (22%)
Consumer Discretionary6 (12%)
Financials5 (10%)
Health Care5 (10%)

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ManagementManagementCZ: 2 positions

Team Lead (Data/AI)

73 positions

Share: 1.21% • Senior+: 98.6%

Installed base: 1,922(3.8%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Team Lead in Data & AI serves as a player-coach for a focused group of engineers or analysts. They split time between hands-on engineering or architectural design and day-to-day team coordination, code reviews, and unblocking colleagues.

The role demands technical depth in data engineering or applied ML paired with mentoring skills, clear communication, and the ability to keep sprint deliveries on track.

• Required Core Skills & Domain

SQL21.9%
Data Governance & Quality15.1%
Python15.1%
GenAI / Generative AI11%
Tableau11%
Excel8.2%
AI Agents / Agentic AI5.5%
Power BI2.7%

• Technology Stack & Tooling

Google Cloud Platform9.6%
Apache Spark8.2%
Snowflake6.8%
Apache Airflow5.5%
Microsoft Azure5.5%
AWS4.1%
Docker2.7%
MLOps / Evals & Observability2.7%

• Top Hiring Companies

IQVIA14 positions
Citigroup13 positions
UnitedHealth Group10 positions
Google (Alphabet)4 positions
TE Connectivity4 positions
Alphabet Inc. (Class C)4 positions

• Top S&P 500 Sectors (Industry)

Health Care24 (33.8%)
Financials21 (29.6%)
Communication Services12 (16.9%)
Consumer Discretionary5 (7%)
Information Technology4 (5.6%)

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ManagementManagement

Data Science / ML Manager

55 positions

Share: 0.91% • Senior+: 100%

Installed base: 2,860(1.9%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Data Science Manager leads teams of data scientists and machine learning practitioners, ensuring predictive and algorithmic initiatives deliver concrete business value rather than remaining academic exercises.

The position requires sound judgment in statistical methods, the ability to manage stakeholder expectations around uncertainty, and strong partnership with engineering teams handling production deployment.

• Required Core Skills & Domain

Python29.1%
SQL27.3%
R21.8%
LLM / Large Language Models16.4%
GenAI / Generative AI16.4%
AI Agents / Agentic AI7.3%
Computer Vision5.5%
Natural Language Processing5.5%

• Technology Stack & Tooling

Apache Spark5.5%
MLOps / Evals & Observability5.5%
Microsoft Azure1.8%
PyTorch1.8%
Databricks1.8%

• Top Hiring Companies

eBay Inc.8 positions
Alphabet Inc. (Class A)6 positions
Alphabet Inc. (Class C)6 positions
Google (Alphabet)6 positions
UnitedHealth Group3 positions
Reddit3 positions

• Top S&P 500 Sectors (Industry)

Communication Services21 (41.2%)
Consumer Discretionary12 (23.5%)
Health Care6 (11.8%)
Industrials5 (9.8%)
Information Technology4 (7.8%)

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AI/MLICCZ: 4 positions

LLM / GenAI Engineer

28 positions

Share: 0.46% • Senior+: 60.7%

Installed base: 1,404(2%)

▼

WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The LLM / GenAI Engineer specializes in designing, fine-tuning, and operating systems built around Large Language Models. Work focuses on domain dataset preparation, RAG architectures, model adaptation, and latency optimization for live traffic.

Market demand for this specialization is surging. Organizations prioritize engineers proficient in Python, modern orchestration frameworks (LangChain, LlamaIndex), vector databases, and evaluation frameworks (Evals) to minimize hallucinations and ensure production stability.

• Required Core Skills & Domain

GenAI / Generative AI100%
AI Agents / Agentic AI46.4%
Python42.9%
LLM / Large Language Models42.9%
RAG (Retrieval-Augmented Generation)39.3%
Natural Language Processing28.6%
SQL10.7%
Fine-Tuning10.7%

• Technology Stack & Tooling

Microsoft Azure25%
LangChain25%
Vector Databases21.4%
PyTorch14.3%
MLOps / Evals & Observability10.7%
Databricks10.7%
pandas7.1%
TensorFlow7.1%

• Top Hiring Companies

Morgan Stanley7 positions
IQVIA3 positions
GE Vernova2 positions
Citigroup2 positions
Datadog2 positions
NXP Semiconductors2 positions

• Top S&P 500 Sectors (Industry)

Financials9 (40.9%)
Industrials5 (22.7%)
Information Technology4 (18.2%)
Health Care3 (13.6%)
Real Estate1 (4.5%)

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ExecutiveManagement

Head of AI / Data

21 positions

Share: 0.35% • Senior+: 100%

Installed base: 1,248(1.7%)

▼

WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Head of AI / Data sets the overarching data and AI strategy for an enterprise or major division. They define long-term technical architecture, governance policies, and build-versus-buy platform decisions.

The role demands executive leadership, a firm grasp of cloud and AI architectures, and the ability to discuss costs, risk management, and tangible business returns transparently with board members.

• Required Core Skills & Domain

Data Governance & Quality38.1%
LLM / Large Language Models33.3%
AI Agents / Agentic AI33.3%
GenAI / Generative AI23.8%
Power BI19%
SQL19%
Python19%
Excel4.8%

• Technology Stack & Tooling

MLOps / Evals & Observability28.6%
Microsoft Azure9.5%
AWS9.5%
Apache Spark9.5%
Google Cloud Platform9.5%
Snowflake4.8%
Databricks4.8%
Microsoft Fabric4.8%

• Top Hiring Companies

Ares Management2 positions
Synchrony Financial2 positions
Prudential Financial1 positions
Advanced Micro Devices1 positions
CDW Corporation1 positions
Franklin Resources1 positions

• Top S&P 500 Sectors (Industry)

Financials12 (63.2%)
Health Care4 (21.1%)
Information Technology2 (10.5%)
Consumer Discretionary1 (5.3%)

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Data EngineeringIC

MLOps Engineer

15 positions

Share: 0.25% • Senior+: 66.7%

Installed base: 295(5.1%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The MLOps Engineer (frequently titled LLMOps or AI Platform Engineer) builds dependable infrastructure for training, versioning, deploying, and monitoring machine learning and generative models in production.

Key responsibilities include CI/CD automation for ML pipelines, container orchestration (Docker, Kubernetes), GPU utilization and inference optimization (such as Triton), and real-time monitoring to detect data drift and quality regressions.

• Required Core Skills & Domain

Python33.3%
GenAI / Generative AI33.3%
RAG (Retrieval-Augmented Generation)20%
AI Agents / Agentic AI20%
LLM / Large Language Models20%
SQL13.3%
Natural Language Processing13.3%
Data Governance & Quality13.3%

• Technology Stack & Tooling

MLOps / Evals & Observability66.7%
Kubernetes40%
AWS26.7%
Docker26.7%
Microsoft Azure20%
Apache Airflow20%
Vertex AI20%
Google Cloud Platform20%

• Top Hiring Companies

United Parcel Service2 positions
Skydance Corporation2 positions
UnitedHealth Group2 positions
TE Connectivity2 positions
Expand Energy1 positions
Gen Digital1 positions

• Top S&P 500 Sectors (Industry)

Industrials4 (28.6%)
Information Technology3 (21.4%)
Health Care2 (14.3%)
Communication Services2 (14.3%)
Energy2 (14.3%)

8 live corporate ATS job postings available in full profile

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ExecutiveManagement

Chief Data / AI Officer (CDO/CAIO)

2 positions

Share: 0.03% • Senior+: 100%

Installed base: 417(0.5%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Chief Data / AI Officer (CDO / CAIO) is a C-suite executive accountable for leveraging data and artificial intelligence to strengthen the company's competitive standing, while ensuring regulatory compliance and ethical standards.

This executive post requires proven leadership in enterprise-scale transformations, building cross-functional organizations, and managing multi-year capital investments in cloud and AI infrastructure.

• Required Core Skills & Domain

Insufficient sample size

• Technology Stack & Tooling

Insufficient sample size

• Top Hiring Companies

UnitedHealth Group1 positions
Berkshire Hathaway1 positions

• Top S&P 500 Sectors (Industry)

Financials1 (50%)
Health Care1 (50%)

2 live corporate ATS job postings available in full profile

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AI/MLIC

Computer Vision Engineer

1 positions

Share: 0.02% • Senior+: 100%

Installed base: 221(0.5%)

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The Computer Vision Engineer develops algorithms for processing imagery, video streams, and 3D spatial data using deep neural networks and multimodal vision models.

Applications span robotics, autonomous vehicles, automated visual inspection, and medical imaging. Essential requirements include solid Python and C++, PyTorch, OpenCV, and the ability to optimize models for real-time edge hardware.

• Required Core Skills & Domain

Computer Vision100%

• Technology Stack & Tooling

Insufficient sample size

• Top Hiring Companies

Kraft Heinz1 positions

• Top S&P 500 Sectors (Industry)

Consumer Staples1 (100%)

1 live corporate ATS job postings available in full profile

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AI/MLIC

NLP Engineer

0 positions

Share: 0% • Senior+: 0%

Installed base: 550

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WHAT COMPANIES LOOK FOR//JOB PROFILE SUMMARY

The NLP Engineer develops systems for natural language understanding: ranging from entity recognition, document classification, and semantic search to architectures built on transformers and large language models.

The role pairs computational linguistics fundamentals with practical software engineering in Python, PyTorch, and the Hugging Face ecosystem, focusing on robust search and language systems designed for live production workloads.

• Required Core Skills & Domain

Insufficient sample size

• Technology Stack & Tooling

Insufficient sample size

• Top Hiring Companies

No active postings

• Top S&P 500 Sectors (Industry)

Outside S&P 500 sample

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