Senior Data Scientist

We are seeking a Senior Data Scientist to conduct research, design, and deployment of advanced analytics and machine learning solutions that power mission-critical decisions in drilling, completions, and geoscience operations.

This role combines applied research, production-grade ML engineering, and technical leadership. You will develop physics-based, rule-based, data-driven, and deep learning models that operate in real-time environments. You will work cross-functionally within Agile software teams and play a key role in translating complex operational challenges into scalable, production-ready solutions.

Senior Data Scientists at Corva operate with a high degree of autonomy, influence technical direction, mentor junior team members, and help define best practices in modeling, experimentation, and deployment.

• Design, develop, and deploy advanced machine learning and deep learning models for anomaly detection, predictive maintenance, forecasting, and optimization
• Develop and implement optimization algorithms to improve operational efficiency and performance
• Conduct applied research in drilling, completions, and geoscience domains
• Design rigorous experiments and validation strategies to test hypotheses and quantify impact
• Analyze large-scale, structured, unstructured, and streaming datasets to extract actionable insights
• Develop real-time models that operate reliably in production environments

• Build, train, tune, and deploy models using AWS Sage Maker
• Develop scalable ML pipelines for data ingestion, feature engineering, training, validation, and monitoring
• Implement model monitoring, drift detection, and performance tracking
• Contribute to CI/CD workflows for machine learning systems
• Ensure reproducibility, robustness, and maintainability of production ML systems
• Maintain strong documentation and model governance practices

• Develop high-quality backend code in Python and contribute to shared codebases
• Participate in code reviews and uphold engineering best practices
• Collaborate closely with product managers, software engineers, and domain experts to deliver production-ready solutions
• Communicate technical findings clearly to both technical and non-technical stakeholders
• Identify opportunities to improve efficiency for both Corva and customer operations

• Provide technical leadership and mentorship to junior R&D teammates
• Drive architectural decisions related to modeling and analytics systems
• Balance accuracy and scientific rigor with MVP timelines and business needs
• Define project milestones and ensure timely delivery
• Support operational teams with clear documentation and procedures
• Ensure continuity of responsibilities during PTO

• Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, Engineering, or a related quantitative field
• 5+ years of experience building and deploying machine learning systems in production environments

• Strong proficiency in Python and modern ML ecosystems
• Deep experience with PyTorch, Tensor Flow, and/or scikit-learn
• Hands‑on experience with AWS Sage Maker (training, tuning, deployment, monitoring)
• Experience building deep learning models for anomaly detection, predictive maintenance, or time‑series forecasting
• Expertise in developing and implementing optimization algorithms (linear, nonlinear, constrained, heuristic, etc.)
• Strong foundation in statistics, experimental design, and causal inference
• Experience working with large-scale data processing frameworks (e.g., Spark)
• Strong SQL and database experience

• Experience with MLOps practices (model versioning, monitoring, drift detection, CI/CD for ML)
• Familiarity with feature stores and data versioning tools
• Experience with model explainability and interpretability techniques
• Understanding of model governance, validation, and risk management
• Experience working with streaming data systems
• Familiarity with LLMs and generative AI concepts is a plus
• Strong experience working in AWS environments
• Familiarity with containerization (Docker) and orchestration (e.g., Kubernetes)

• Basic knowledge of drilling, completions, or geoscience operations preferred
• Proven track record of delivering measurable business impact
• Ability to identify anomalies and root causes in complex operational datasets

• Excellent analytical thinking and problem-solving skills
• Strong written and verbal communication skills
• Proven ability to work cross‑functionally in fast‑paced environments
• Experience mentoring junior data scientists
• Publications or contributions to the data science community (research papers, open‑source projects) are a plus
• Medical, dental, and vision insurance
• Retirement savings plan
• Collaborative, fun and innovative work environment