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 new 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!
