Note: The job is a remote job and is open to candidates in USA. Vantora is partnering with a global energy leader to build a next-generation intelligence platform for exploration decisions. The Lead Data Scientist will design, train, and deploy AI and machine learning models using oil and gas data, working with product, engineering, and domain experts to turn operational data into actionable intelligence.
Responsibilities
- Design and implement ML models for prediction, optimization, and pattern detection across large, multi-source oil and gas datasets (e.g., production, drilling, reservoir, sensor/SCADA, and asset data)
- Collaborate cross-functionally with product managers, engineers, data engineers, and oil and gas domain experts to define use cases, collect requirements, and translate operational and business needs into quantitative problems
- Build and maintain data pipelines that support scalable model training, evaluation, and deployment
- Prototype rapidly, exploring different modeling approaches (e.g., regression, time-series forecasting, clustering, reinforcement learning, and deep learning) to find the most effective solutions
- Evaluate and improve model performance through rigorous experimentation, feature engineering, and error analysis, grounded in the physical and operational realities of oil and gas assets
- Partner with the AI/LLM engineering team to integrate predictive and generative components into end-user workflows
- Communicate findings clearly to both technical and non-technical audiences, bridging the gap between model outputs and business decisions
- Establish best practices for reproducibility, versioning, and documentation to support long-term scalability
Skills
- 6+ years of professional experience in applied data science, machine learning, or advanced analytics
- Direct experience applying data science or ML in the oil and gas industry (upstream, midstream, or downstream), with a working understanding of operational data such as production, drilling, reservoir, or equipment/sensor data
- Proven ability to build and deploy models in production
- Expertise in Python, SQL, and common ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.)
- Strong experience with data wrangling, feature engineering, and exploratory data analysis using large-scale structured and unstructured datasets
- Experience with cloud-based ML pipelines (GCP, AWS, or Azure) and data infrastructure tools (Databricks, Spark, Airflow, etc.)
- Ability to design experiments and A/B tests, interpret results, and guide decision-making through data
- Strong communication and storytelling skills, able to contextualize technical insights for executives, field operations teams, and business stakeholders
- Ideally within a B2B SaaS, energy technology, or enterprise environment
- Familiarity with time-series, forecasting, and predictive maintenance models
- Experience at an energy operator, oilfield services company, or energy-focused software/analytics firm
- Exposure to physics-informed ML or hybrid models that combine engineering principles with data-driven approaches
Benefits
- Remote or hybrid work arrangement.
Company Overview