About Corva
Corva has built a first-of-its-kind energy app store on a bedrock of best-in-class technologies, data pipelines, and a secure and scalable architecture. Our energy solutions solve today's toughest well delivery challenges, from well design through drillout. The ever-evolving platform is not only future-proof for digitizing operations but is your toolkit to accelerate sustainability and energy transition goals. Our platform is built for speed and reliability and delivers unmatched features and capabilities.
Corva is powering worldwide innovation by driving efficiency, productivity, and profitability with our innovative energy solutions.
Mission
Corva’s mission is to accelerate the future of energy.
Values
Boldness
Extreme Ownership
Full Transparency
Bias Action
Radical Candor
Rapid Iteration
Uncompromising Quality
High Agency
About the role
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.
What you'll do
Advanced Modeling & Research
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Design, develop, and deploy advanced machine learning and deep learning models for anomaly detection, predictive maintenance, forecasting, and optimization
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Develop and implement optimization algorithms to improve operational efficiency and performance
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Conduct applied research in drilling, completions, and geoscience domains
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Design rigorous experiments and validation strategies to test hypotheses and quantify impact
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Analyze large-scale, structured, unstructured, and streaming datasets to extract actionable insights
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Develop real-time models that operate reliably in production environments
Production ML & MLOps
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Build, train, tune, and deploy models using AWS SageMaker
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Develop scalable ML pipelines for data ingestion, feature engineering, training, validation, and monitoring
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Implement model monitoring, drift detection, and performance tracking
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Contribute to CI/CD workflows for machine learning systems
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Ensure reproducibility, robustness, and maintainability of production ML systems
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Maintain strong documentation and model governance practices
Engineering & Collaboration
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Develop high-quality backend code in Python and contribute to shared codebases
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Participate in code reviews and uphold engineering best practices
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Collaborate closely with product managers, software engineers, and domain experts to deliver production-ready solutions
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Communicate technical findings clearly to both technical and non-technical stakeholders
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Identify opportunities to improve efficiency for both Corva and customer operations
Leadership & Ownership
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Provide technical leadership and mentorship to junior R&D teammates
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Drive architectural decisions related to modeling and analytics systems
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Balance accuracy and scientific rigor with MVP timelines and business needs
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Define project milestones and ensure timely delivery
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Support operational teams with clear documentation and procedures
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Ensure continuity of responsibilities during PTO
Qualifications
Education & Experience
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Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, Engineering, or a related quantitative field
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5+ years of experience building and deploying machine learning systems in production environments
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Demonstrated ownership of end-to-end ML lifecycle (research experimentation deployment monitoring)
Technical Expertise
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Strong proficiency in Python and modern ML ecosystems
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Deep experience with PyTorch, TensorFlow, and/or scikit-learn
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Hands-on experience with AWS SageMaker (training, tuning, deployment, monitoring)
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Experience building deep learning models for anomaly detection, predictive maintenance, or time-series forecasting
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Expertise in developing and implementing optimization algorithms (linear, nonlinear, constrained, heuristic, etc.)
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Strong foundation in statistics, experimental design, and causal inference
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Experience working with large-scale data processing frameworks (e.g., Spark)
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Strong SQL and database experience
Modern ML & Data Practices
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Experience with MLOps practices (model versioning, monitoring, drift detection, CI/CD for ML)
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Familiarity with feature stores and data versioning tools
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Experience with model explainability and interpretability techniques
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Understanding of model governance, validation, and risk management
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Experience working with streaming data systems
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Familiarity with LLMs and generative AI concepts is a plus
Cloud & Infrastructure
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Strong experience working in AWS environments
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Experience designing scalable cloud-based ML architectures
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Familiarity with containerization (Docker) and orchestration (e.g., Kubernetes)
Domain & Business Impact
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Basic knowledge of drilling, completions, or geoscience operations preferred
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Proven track record of delivering measurable business impact
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Ability to identify anomalies and root causes in complex operational datasets
Leadership & Communication
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Excellent analytical thinking and problem-solving skills
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Strong written and verbal communication skills
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Proven ability to work cross-functionally in fast-paced environments
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Experience mentoring junior data scientists
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Publications or contributions to the data science community (research papers, open-source projects) are a plus
Benefits
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Competitive salary
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Medical, dental, and vision insurance
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Retirement savings plan
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Flexible remote work schedule
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Professional development opportunities
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Collaborative, fun and innovative work environment
