At H&P;, our people are our strength. As a P1 hire, you will rotate across multiple AI projects, contributing hands-on to data pipelines, models, and prototypes while learning the drilling domain from SMEs.

What You'll Do Build and maintain data pipelines that transform raw one-second sensor data into analysis-ready datasets (drilling events, stand-level aggregations, contextual joins with BHA, survey, and mud data)

Develop, test, and iterate on machine learning models for time-series problems: anomaly detection, failure prediction, dysfunction classification, and performance benchmarking

Support retrieval and LLM-based workflows : embedding pipelines, text-to-SQL over drilling related data model, and evaluation of agent outputs.

Create dashboards, visualizations, and internal tools that make model outputs usable by field engineers and ROC operators

Perform exploratory analysis to answer engineering questions

Write clean, documented, version-controlled code and contribute to model monitoring once projects reach production

Participate in stakeholder interviews and requirement sessions with SMEs and translate field pain points into technical tasks.

What You'll Bring (Required) Bachelor's degree in Data Science, Petroleum/Mechanical Engineering, or a related quantitative field (0–2 years of experience; strong internship / professional analyst experience)

Solid Python fundamentals,



including pandas/NumPy and at least one ML framework (scikit-learn, XGBoost, Langchain)

Working knowledge of SQL and comfort querying large relational datasets

Understanding of core ML concepts: supervised learning, cross-validation, feature engineering, and evaluation metrics

Ability to communicate analytical findings clearly to non-technical audiences

Curiosity about industrial operations and willingness to learn drilling domain concepts (ROP, MSE, DvD, BHA, flat time) on the job. Nice to Have Exposure to time-series analysis or sensor/IoT data

Experience with cloud data platforms (Microsoft Fabric, Azure, Databricks, or Snowflake)

Familiarity with LLM application patterns: RAG, embeddings, vector databases, prompt engineering, or agent frameworks

Dashboarding experience (Power BI, Plotly or React-based tooling)

Prior internship or project in energy, manufacturing, or another heavy-industrial domain

Git-based collaboration and basic CI/CD awareness Why Join You'll work at the intersection of AI and heavy industry. You will get the opportunity to work on a defined roadmap spanning quick wins to advanced autonomy, and a team culture that pairs recent hires with experienced SMEs and data scientists. Few early-career roles offer this breadth: real-time systems, classical ML, and frontier LLM applications inside a single position. Thank you for your interest in joining our team!

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