Interested in this Data Scientist role at HP?
Apply Now →Skills & Technologies
About This Role
AI Data Scientist – Enterprise AI
Description \-
Enterprise Operations Applied AI Organization
Overview
The Enterprise Operations Applied AI organization is seeking an AI Data Scientist to help design, build, evaluate, and scale AI\-driven solutions that deliver measurable business impact across enterprise operations. This role sits at the intersection of applied research, machine learning engineering, data science, and business transformation.
The ideal candidate combines strong technical expertise in Large Language Models (LLMs), Generative AI, machine learning, and large\-scale data analysis with the ability to work effectively in complex enterprise environments on multidisciplinary teams. Success in this role requires curiosity, initiative, strong communication skills, and a passion for turning emerging AI technologies into practical business solutions.
This position supports the Enterprise Operations Applied AI organization's mission of enabling AI\-powered transformation through applied research, scalable solutions, responsible AI practices, and cross\-functional collaboration.
Key Responsibilities
- Design, develop, and deploy AI\-powered solutions leveraging Large Language Models (LLMs), Generative AI technologies, machine learning, and predictive analytics.
- Develop data pipelines, feature engineering approaches, and analytical workflows that support AI solution development.
- Research new AI methods, tools, and frameworks and determine their applicability to business problems across enterprise operations.
- Design and execute experiments to evaluate model effectiveness, accuracy, robustness, and operational performance.
- Analyze large\-scale structured and semi\-structured datasets to generate insights, build predictive models, and support operational decision\-making.
- Translate business requirements into technical approaches and clearly communicate AI concepts to both technical and non\-technical audiences.
- Support adoption of AI solutions through training, demonstrations, documentation, and stakeholder engagement.
- Collaborate with distributed teams of engineers, data scientists, product owners, business leaders, and other technology organizations to deliver impactful solutions.
- Contribute to AI best practices, reusable frameworks, and technical standards across the organization.
Required Qualifications
- Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Engineering, Mathematics, or a related field.
- 3\+ years of experience developing AI, machine learning, and data science solutions.
- Proficiency in Python and modern AI/ML libraries and frameworks.
- Experience with model evaluation, experimentation, performance measurement, and validation methodologies.
- Strong analytical skills with experience working with large\-scale tabular datasets using SQL, Spark, Databricks, or similar technologies.
- Ability to collaborate effectively in culturally diverse and distributed teams.
Preferred Qualifications
- 5\+ years of experience developing AI, machine learning, and data science solutions in an industry setting.
- Experience developing AI solutions in cloud environments such as Azure, AWS, or GCP.
- Proficiency using software development tools such as version control (e.g. Github) and AI\-assisted development tools (e.g. Github Copilot)
- Experience with Retrieval\-Augmented Generation (RAG), prompt engineering, AI agents.
- Familiarity with MLOps, model monitoring, observability, and enterprise AI governance concepts.
- Experience communicating technical concepts to business stakeholders.
- Experience working in highly collaborative, matrixed organizations.
What Success Looks Like
A successful AI Data Scientist – Enterprise AI:
- Builds AI solutions that move beyond prototypes and create measurable business value.
- Demonstrates technical depth in LLMs, machine learning, and data science while maintaining a practical focus on implementation.
- Communicates clearly with product managers, engineers, and operational teams.
- Takes ownership of outcomes and proactively drives work forward without waiting for direction.
- Continuously identifies opportunities to improve processes, solutions, and ways of working.
Core Competencies
- Applied AI \& Machine Learning
- Large Language Models (LLMs) \& Generative AI
- Data Science \& Statistical Analysis
- Enterprise Problem Solving
- Experimentation \& Model Evaluation
- Communication \& Storytelling
- Cross\-Functional Collaboration
- Ownership \& Accountability
This role is ideal for someone who enjoys combining research, engineering, analytics, and business partnership to transform enterprise operations through practical and scalable AI solutions.
Pay \& Benefits
The pay range for this role is $130,700 to $205,200 USD annually with additional
opportunities for pay in the form of bonus and/or equity (applies to United
States of America candidates only). Pay varies by work location, job\-related
knowledge, skills, and experience.
Benefits:
HP offers a comprehensive benefits package for this position, including:
- Health insurance
- Dental insurance
- Vision insurance
- Long term/short term disability insurance
- Employee assistance program
- Flexible spending account
- Life insurance
- Generous time off policies, including;
- 4\-12 weeks fully paid parental leave based on tenure
- 11 paid holidays
- Additional flexible paid vacation and sick leave
- US benefits overview https://hpbenefits.ce.alight.com/
The compensation and benefits information is accurate as of the date of this
posting. The Company reserves the right to modify this information at any time,
with or without notice, subject to applicable law.
Job \-
Software
Schedule \-
Full time
Shift \-
No shift premium (United States of America)
Travel \-
No
Relocation \-
Not Specified
Equal Opportunity Employer (EEO) \-
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal "
Salary Context
This $130K-$205K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At HP, 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($167K) sits 13% below the category median. Disclosed range: $130K to $205K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
HP AI Hiring
HP has 3 open AI roles right now. They're hiring across AI Software Engineer, MLOps Engineer, Data Scientist. Positions span San Francisco, CA, US, Spring, TX, US. Compensation range: $205K - $240K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.