AI Data Scientist – Enterprise AI
AI in this role
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 -
SoftwareSchedule -
Full timeShift -
No shift premium (United States of America)Travel -
NoRelocation -
Not SpecifiedEqual 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"
How we rate this
AI Data Scientist – Enterprise AI at HP rates 93 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
Adapt your resume
- List these exact terms on your resume: Prompt Engineering, RAG, AI Agents, ML Ops, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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