Job Summary:

We are looking for a hands-on Data Scientist to lead the development of machine learning models and data-driven insights for Digital Oilfield (DOF) systems. This role requires a strong foundation in data science and statistical modeling, with the ability to transform noisy, real-world oilfield data into actionable intelligence for engineering teams.

Candidate will work alongside production engineers, software developers, and ML engineers to design, validate, and operationalize predictive and prescriptive analytics that support key decisions in subsurface and surface operations. Oil and gas experience is a strong asset but not mandatory; the ideal candidate brings analytical rigor and problem-solving expertise to solve complex field challenges.

Key Responsibilities:

• Analyze diverse operational datasets (time series, tabular, sensor logs, etc.) to extract insights and guide model development.
• Build and evaluate ML models for forecasting, anomaly detection, pattern recognition, and classification.
• Work with engineers to define KPIs and convert domain-specific questions into quantifiable modeling tasks.
• Design meaningful visualizations and dashboards to communicate model outputs clearly.
• Collaborate with ML Ops and software teams to deploy models into production environments and monitor performance.

Required Skills and Experience:

• Strong statistical background and data science expertise with real-world datasets.
• Proficient in Python (NumPy, pandas, scikit-learn, XGBoost, etc.); SQL fluency and data wrangling skills.
• Experience working with large, messy, or multivariate time series data.
• Ability to communicate complex model behavior to engineers and stakeholders.
• Comfortable working in cross-functional teams with domain and technical experts.
• Oilfield data exposure (e.g., well data, reservoir simulations, production logs) or interest in industrial applications.
• Familiarity with DOF systems or production optimization frameworks.
• Exposure to LLMs, NLP techniques, or agent-based AI for enhancing technical workflows.
• Cloud familiarity (Azure preferred); knowledge of ML platforms (e.g., Azure ML, Databricks).

Certifications:

• Azure Data Scientist Associate or Microsoft AI Fundamentals certification is a plus.
• AWS cloud knowledge is welcome but not required.