Description
HP's Applied AI team is looking for a Data Scientist to boost AI/ML adoption in Commercial Organization processes & activities.
The ideal candidate will shape HP’s future of how to solve concrete business problems with the use of data science, by joining a very dynamic, highly skilled and diverse global team who is pushing the innovation frontier in the way we use AI & automation solutions to solve our stakeholders’ challenges.
Working with us you will combine your modelling skills, business thinking, experimentation and optimization methods to build viable solutions for HP’s Commercial Organization.
Main Responsibilities:
- Accelerate GenAI & ML Adoption: Drive the adoption of Generative AI and Machine Learning technologies within HP’s Commercial Organization by identifying high-value business challenges and developing scalable, impactful solutions that enhance productivity, decision-making, and innovation.
- Use Case Validation & Reusability: Collaborate with the AI & Automation Product Owner to validate backlog priorities, assess technical feasibility, and determine whether existing ML or GenAI assets can be scaled or adapted for new use cases.
- End-to-End Solution Delivery: Partner with cross-functional teams,including data scientists, ML/GenAI engineers, data/cloud experts, and RPA developers,to turn business problems into viable, sustainable solutions using ML algorithms or GenAI architectures.
- Stakeholder Engagement: Work directly with business stakeholders to understand their needs, translate them into solution requirements, and provide actionable recommendations that align with business strategy.
- Model Development & Deployment: Design and build both predictive ML models and GenAI solutions,from data preprocessing and feature engineering to model development, fine-tuning, evaluation, and deployment,leveraging Azure, Databricks, and other enterprise platforms.
- GenAI-Specific Capabilities: Architect and implement Retrieval-Augmented Generation (RAG) systems, fine-tune LLMs, and apply advanced prompt engineering techniques to build chat-based tools, search augmentation solutions, and virtual assistants that deliver real business value.
- Performance Monitoring & Validation: Conduct rigorous model evaluation and performance monitoring for both ML and GenAI models to ensure reliability, fairness, and compliance.
- Communication of Insights: Translate complex ML/GenAI outcomes into business-friendly insights and visualizations.
- Community Engagement & Coaching: Mentor junior team members and citizen developers, sharing GenAI/ML knowledge, reusable assets, and architectural patterns.
- Innovation & R&D: Stay updated with the latest research in ML and GenAI.
Job Requirements:
- Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Mathematics, Physics, Engineering, or a related quantitative field.
- Experience: Typically, 6–8 years of hands-on experience developing and deploying ML and/or GenAI solutions in a business context.
- Programming & Development: Proficiency in Python for data manipulation, model development, and integration.
- Cloud & Platform Expertise: Strong hands-on experience with Azure services.
- ML/AI Model Development: Ability to design, train, and deploy supervised and unsupervised ML models.
- GenAI & NLP: Familiarity with Large Language Models (LLMs), prompt engineering, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
- Applied Statistics & Algorithms: Solid grasp of statistical modeling, feature engineering, and machine learning algorithms.
- Business Acumen: Proven ability to understand business needs and align AI/ML solutions with strategic goals.
- Collaboration & Communication: Comfortable working in cross-functional teams.
Preferred Skills:
- Experience with MLOps and model lifecycle management.
- Exposure to ethical AI practices, responsible AI frameworks, or compliance in enterprise settings.