The WXP (Workforce Experience Platform) powers HP’s next-generation, cloud‑based analytics and AI capabilities across smart device ecosystems. WXP integrates large-scale telemetry, operational data, and advanced AI techniques—including machine learning and generative AI—to deliver actionable insights, intelligent automation, and predictive intelligence at enterprise scale.

As a Data Scientist, you will play a critical role in examining, evaluating, and processing data from multiple sources to identify high‑impact opportunities that optimize predictive insights, intelligent recommendations, and AI‑driven experiences. You will guide and consult platform and product teams on advanced analytics, machine learning, and GenAI techniques to build differentiated, scalable capabilities on WXP. You will also be part of a high‑caliber, highly collaborative team focused on delivering measurable business value through data and AI innovation.

Job Responsibilities:

• Create, train, test, validate, and continuously improve machine learning and generative AI models using data from the WXP platform

• Deploy and operate ML and GenAI models in production to deliver real‑time and near‑real‑time insights across large‑scale device and service ecosystems (1M+ devices)

• Develop novel algorithms and investigate emerging AI technologies to:

  • Detect patterns and anomalies

  • Predict failures and performance degradation

  • Discover relationships in large‑scale engineering and operational datasets

• Design and implement GenAI‑powered solutions, including:

  • Large Language Model (LLM)–based insights

  • AI agents and retrieval‑augmented generation (RAG) workflows

  • Natural language interfaces for analytics and operational intelligence

• Partner with platform, engineering, and product leadership to identify high‑value AI opportunities and translate them into scalable solutions

• Consult with senior management on topics related to:

  • AI‑driven platform capabilities

  • Machine learning and GenAI strategy

  • Data‑informed decision making

• Drive adoption of modern ML, GenAI, and MLOps best practices, including model lifecycle management, monitoring, governance, and responsible AI

What We’re Looking For

• Strong, proven experience developing real‑world machine learning and GenAI solutions, including:

  • Exploratory data analysis (EDA)

  • Data cleaning and handling missing/noisy data

  • Feature engineering

  • Model development, evaluation, and validation

• Experience applying MLOps and GenAIOps best practices, including:

  • Model and data versioning

  • CI/CD for ML and GenAI pipelines

  • Documentation, monitoring, and drift detection

• Solid grounding in probabilistic and statistical methods for inference and decision‑making (Bayesian and Frequentist approaches)

• Strong understanding of classical ML techniques, including:

  • Naive Bayes, Logistic Regression, SVMs

  • Decision Trees, Random Forests, CART models

  • Graphical Models (Bayesian and Markov Networks)

  • Latent variable and dimensionality reduction techniques (PCA, ICA, LDA, matrix factorization)

• Familiarity with modern deep learning and generative AI approaches, such as:

  • Neural networks and transformers

  • Large Language Models (LLMs)

  • Prompt engineering, fine‑tuning, and RAG patterns

• Strong implementation skills in deploying and scaling cloud‑based ML and GenAI workloads

• Hands‑on experience with cloud ecosystems (AWS and/or Azure), including:

  • Serverless and event‑driven architectures

  • Distributed data processing

• Proficiency with SQL and NoSQL databases

• Strong ownership mindset with the ability to drive outcomes while collaborating effectively across teams

Desired Education, Experience, and Skillsets

• Graduate/ Post Graduate in Computer Science, Statistics, or a related quantitative field

  • Typically 5+ years post‑Master’s or 3+ years post‑PhD experience in a data science or applied AI role

• Programming Languages: Python, R, PySpark, Java (Matlab a plus)

• Distributed compute frameworks: Spark, Hadoop

• ML / AI Platforms: SageMaker, Azure ML, or equivalent

• Databases: SQL databases, DynamoDB, MongoDB

• Cloud compute and data architecture: AWS and/or Azure

• Experience working with enterprise‑scale AI platforms and production systems preferred