New opportunity

Senior AI & Data Scientist

HPE · Westford, United States of America

About this role

HPE seeks a Senior AI & Data Scientist to develop and deploy AI solutions that improve sales performance, seller productivity, and operational efficiency. This full-time role is based in Westford, Massachusetts, and requires working from an HPE office an average of two days per week.

Responsibilities

Design, develop, and deploy machine learning and generative AI solutions for sales and commercial operations. Build predictive models for sales forecasting, pipeline health, lead scoring, opportunity prioritization, territory planning, and customer segmentation.
Create AI-powered copilots, intelligent assistants, and retrieval-augmented generation (RAG) solutions to streamline sales processes. Analyze CRM, sales, customer, and operational data to identify trends, risks, and growth opportunities.
Partner with Sales, Sales Operations, Revenue Operations, and IT leaders to select high-value use cases and deliver scalable solutions aligned with business objectives. Maintain production-ready models and MLOps processes, including reliability, scalability, and performance monitoring.
Develop dashboards and executive reporting on pipeline performance, forecast accuracy, sales productivity, and other commercial metrics. Evaluate emerging AI technologies for potential improvements to commercial effectiveness and customer engagement.

Required qualifications

Master’s degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, or a related quantitative field; 7+ years of data science, machine learning, or AI experience, including deployment of enterprise-scale AI solutions.
Strong Python and SQL proficiency and experience with frameworks such as PyTorch, TensorFlow, and Scikit-learn. Experience with LLMs, prompt engineering, RAG, AI agents, and generative AI frameworks.
Experience with Salesforce or similar CRM platforms, sales analytics, enterprise data environments, cloud platforms, MLOps, and visualization tools such as Power BI or Tableau. Azure is preferred among cloud platforms.

Preferred qualifications

Experience supporting sales or revenue operations in enterprise technology, networking, SaaS, or cloud; knowledge of forecasting, pipeline analytics, territory management, account planning, pricing, and sales performance metrics.
Executive communication and presentation skills, plus experience leading cross-functional AI initiatives and promoting data-driven decisions.

The Massachusetts base salary range is USD 120,000–228,000 annually; variable incentives may also be offered. HPE describes health and wellbeing benefits and career-development programs.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLPyTorchTensorFlowScikit-learnMachine LearningGenerative AILarge Language ModelsPrompt EngineeringRetrieval-Augmented GenerationAI AgentsPredictive ModelingSales ForecastingSales AnalyticsCustomer SegmentationSalesforceCRMAzureMLOpsPower BITableauData VisualizationCross-functional CollaborationExecutive Communication

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Improve commercial decisions with predictive models
The role develops models for forecasting, pipeline health, lead scoring, opportunity prioritization, territory planning, and segmentation. Prepare to explain how model outputs would support specific sales decisions and how their value would be measured.
Make generative AI useful to sellers
HPE calls for copilots, intelligent assistants, and RAG solutions that improve seller productivity and streamline processes. Focus preparation on a practical seller workflow, rather than a standalone demonstration.
Choose high-value use cases across teams
The role partners with Sales, Sales Operations, Revenue Operations, and IT leaders to select use cases and align scalable solutions with business objectives. Prepare to describe how competing needs can be translated into a clear scope and success measure.
Operate AI solutions at enterprise scale
HPE explicitly requires production-ready models and MLOps processes covering reliability, scalability, and ongoing performance monitoring. Be ready to discuss deployment and maintenance decisions, not just model development.
Turn commercial data into executive insight
The role analyzes CRM, sales, customer, and operational data and produces dashboards and executive reporting on pipeline performance, forecast accuracy, and productivity. Prepare to connect a metric or finding to an actionable decision.

Likely challenges to think through

Forecast usefulness versus data limitations
Possible challenge, not a stated HPE problem: CRM and sales records may be incomplete or change as opportunities progress, making a strong-looking forecast less useful in practice. Prepare to discuss data-quality checks, time-aware validation, and how to communicate uncertainty to sales leaders.
Copilot speed versus dependable answers
Possible challenge, not a stated HPE problem: a seller assistant must save time without presenting unsupported or outdated information as fact. Prepare to explain how retrieval quality, grounded responses, evaluation, and escalation to a person would be balanced against workflow friction.
Novel AI use cases versus sustained adoption
Possible challenge, not a stated HPE problem: an emerging AI capability may be technically promising but costly to operate or poorly matched to how teams make decisions. Prepare a framework for comparing business value, scalability, monitoring effort, and measurable uptake before recommending a rollout.

Practice your answers

Technical practice

Suggested practice question: How would you build and evaluate a sales forecast?

What to coverThe role includes predictive modeling for sales forecasting and reporting on forecast accuracy. Preparation suggestion: explain target definition, time-based validation, suitable accuracy measures, and how you would compare a model with an existing forecast.

Suggested practice question: How would you turn pipeline health and lead scores into useful priorities?

What to coverThe posting names pipeline health, lead scoring, and opportunity prioritization. Preparation suggestion: discuss outcome labels, leakage from changing CRM records, score calibration, and how to check whether rankings help sellers focus effort.

Suggested practice question: How would you approach customer segmentation and territory planning?

What to coverBoth are listed predictive-modeling applications, and territory management is preferred knowledge. Preparation suggestion: distinguish descriptive segments from planning decisions, then discuss stability, interpretability, and commercial trade-offs.

Suggested practice question: How would you investigate an apparent change in pipeline performance using CRM data?

What to coverThe role analyzes CRM, sales, customer, and operational data for trends and risks; Python, SQL, and CRM experience are listed qualifications. Preparation suggestion: walk through joins and data-quality checks, changes in reporting definitions, and how you would separate a real trend from a data artifact.

Suggested practice question: How would you design a retrieval-augmented assistant for sellers?

What to coverThe posting calls for RAG solutions and intelligent assistants to streamline sales processes. Preparation suggestion: cover document selection, retrieval quality, answer grounding, access to appropriate information, and evaluation against seller tasks.

Suggested practice question: When would you use an AI agent rather than a prompt-based copilot?

What to coverCopilots, AI agents, LLMs, and prompt engineering appear in the role description or qualifications. Preparation suggestion: compare the value of multi-step actions with their added complexity, and explain how you would evaluate output quality and control unreliable behavior.

Suggested practice question: How would you maintain a commercial AI model after deployment?

What to coverThe role calls for production-ready models and MLOps processes with reliability, scalability, and performance monitoring. Preparation suggestion: describe monitoring for input changes and model performance, retraining decisions, and how you would investigate degraded results.

Suggested practice question: What would an executive dashboard need to make pipeline and productivity metrics actionable?

What to coverThe posting specifies dashboards and executive reporting on pipeline performance, forecast accuracy, and sales productivity, with Power BI or Tableau among the listed tools. Preparation suggestion: discuss metric definitions, appropriate comparisons, drill-downs, and how to communicate uncertainty without obscuring decisions.

Suggested practice question: How would you evaluate an emerging AI technology for commercial use?

What to coverThe role includes recommending emerging AI technologies that could improve commercial effectiveness and customer engagement. Preparation suggestion: compare a new approach with a simpler baseline using business value, output quality, operational cost, and deployment risk.

Suggested practice question: How would you choose a model framework and cloud deployment approach for an enterprise-scale solution?

What to coverThe posting lists Python, SQL, frameworks such as PyTorch, TensorFlow, and Scikit-learn, plus cloud experience with Azure preferred. Preparation suggestion: explain how model needs, team maintainability, scalability, and monitoring would guide choices rather than treating any named tool as mandatory.

Behavioural practice

Suggested practice question: Tell me about a time you chose among competing AI use cases.

What to coverThe role partners with commercial and IT leaders to identify high-value uses aligned with business objectives. Use a real situation, your actions in weighing value against feasibility, and the resulting decision or outcome.

Suggested practice question: Describe a cross-functional AI initiative you led through disagreement.

What to coverThe posting values leading initiatives across commercial functions, including Sales, Sales Operations, Revenue Operations, and IT. Use a real situation, how you resolved differing priorities, and the result.

Suggested practice question: Tell me about explaining a technical finding to an executive audience.

What to coverExecutive reporting, communication, and influencing stakeholders are part of the posting. Use a real situation, how you framed the evidence and uncertainty, and what decision or response followed.

Suggested practice question: Describe how you encouraged adoption of a data-driven decision.

What to coverThe posting prefers experience driving data-driven decision-making across commercial organizations. Use a real situation, the actions you took to make the insight usable, and the observed result.

Suggested practice question: Tell me about a production model that needed attention after launch.

What to coverMaintaining reliable AI models and monitoring performance are stated responsibilities. Use a real situation, how you investigated and coordinated a response, and the resulting operational or business outcome.

Sharpen your knowledge

Topics to prepare

Sales forecasting and pipeline health
The posting calls for forecasting and pipeline-health models, plus reporting on forecast accuracy. Suggested revision exercise: explain how you would define a forecast target, prevent future pipeline information from entering training data, and compare errors across forecast periods.
Commercial prioritization and segmentation
Lead scoring, opportunity prioritization, territory planning, and customer segmentation are named responsibilities. Suggested revision exercise: compare the objective, decision, and success measure for each, including how a score would guide a seller’s action.
LLMs, retrieval, and agents
The role explicitly includes prompt engineering, RAG, AI agents, copilots, and intelligent assistants. Suggested revision exercise: sketch a seller-facing RAG workflow and explain how you would assess answer relevance, source support, and failure cases.
Production AI and MLOps
HPE calls for enterprise-scale deployment and production-ready models with reliability, scalability, and ongoing performance monitoring. Suggested revision exercise: walk through how you would detect degraded forecast quality and decide whether to retrain, investigate data, or roll back.
Sales-operations use-case selection
The role partners with Sales, Sales Operations, Revenue Operations, and IT to identify high-value use cases; account planning and pricing knowledge are preferred. Suggested revision exercise: compare two hypothetical sales AI ideas by business value, data availability, adoption needs, and measurable outcomes.

Skills to revise

Query CRM and sales data with SQL
Strong SQL proficiency and experience with Salesforce or a similar CRM are stated requirements. Suggested revision exercise: on a sample CRM-style dataset, join accounts, opportunities, and activity records to calculate pipeline by stage and identify missing or duplicated records.
Build and evaluate a predictive model in Python
The posting requires Python and cites frameworks such as Scikit-learn, PyTorch, and TensorFlow. Suggested revision exercise: use Python and a cited framework to build a lead-scoring or forecasting baseline on sample data, then explain its validation and business-facing metric.
Prototype a seller assistant
Building copilots, intelligent assistants, and RAG solutions is an explicit responsibility. Suggested revision exercise: draft prompts and a small retrieval-based prototype over sample sales-process documents; test questions whose answers are present, absent, or ambiguous.
Plan deployment and monitoring
Cloud platforms, MLOps, and performance monitoring are specified; Azure is preferred, not required. Suggested revision exercise: diagram a model’s path from training to deployment on a cloud platform and specify checks for service reliability, input changes, and model performance.
Create an executive commercial dashboard
The role calls for dashboards and executive reporting; Power BI and Tableau are cited visualization tools. Suggested revision exercise: mock up a dashboard showing pipeline performance, forecast accuracy, and sales productivity, then write one decision-oriented takeaway.
Present a cross-functional AI proposal
Executive communication and leading cross-functional AI initiatives are preferred qualifications. Suggested revision exercise: give a brief presentation of a proposed sales AI use case, stating the business objective, data dependencies, evaluation measure, and decisions needed from Sales, Revenue Operations, and IT.

Your preparation checklist

Map evidence to the stated qualifications
Prepare a one-page inventory of verifiable education, years of AI or data science work, enterprise deployments, Python and SQL, ML frameworks, LLMs, CRM, cloud, MLOps, and visualization tools. The posting requires these areas and prefers Azure among cloud platforms.
Prepare a sales-model project walkthrough
Choose a real, shareable project if available. Explain the business decision, CRM or sales data, Python and SQL work, modeling approach, evaluation, deployment, and measured results. Relate it to a posted use case such as forecasting, pipeline health, or lead scoring without claiming it was an HPE project.
Sketch a seller-assistant design
Prepare a hypothetical copilot or RAG design for a sales workflow. Show the information it would retrieve, how prompts and any agents would be used, how outputs would be evaluated, and where a seller would review them. Label the design as an interview exercise, not an HPE plan.
Explain the production operating model
Draw a simple deployment and MLOps diagram for one proposed solution, covering reliability, scale, performance monitoring, and how model quality would be checked over time. Note where a cloud platform could fit; the posting names Azure as preferred, not mandatory.
Draft an executive-ready commercial readout
Prepare a sample dashboard outline and brief presentation showing pipeline performance, forecast accuracy, or sales productivity. State metric definitions, the decision each view supports, and any data limitations; Power BI and Tableau are examples named in the posting.
Rehearse cross-functional leadership examples
Prepare concise, factual examples of selecting a high-value AI use case with business and IT partners, resolving trade-offs, and communicating results to executives. The role names Sales, Sales Operations, Revenue Operations, and IT as partners.
Write questions that clarify priorities
Bring questions about the first sales use cases to prioritize, available CRM and operational data, success measures, production ownership, and how the named teams collaborate. These are points to clarify with HPE, not assumptions about its current processes.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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