Senior Data Scientist

Why YOU want this position

At Enverus, we’re committed to empowering the global quality of life by helping our customers make energy affordable and accessible to the world.

We are the most trusted energy-dedicated SaaS company, with a platform built to maximize value from generative AI, and our innovative solutions are reshaping the way energy is consumed and managed. By offering anytime, anywhere access to analytics and insights, we’re helping our customers make better decisions that help provide communities around the world with clean, affordable energy.

The energy industry is changing fast. But we’ve continued to lead the way in energy technology, creating intelligent connections across the entire energy ecosystem, from renewables, power and utilities, to oil and gas and financial institutions. Our solutions create more efficient production and distribution, capital allocation, renewable energy development, investment and sourcing, and help reduce costs by automating crucial business operations. Of course, this wouldn’t be possible without our people, which is why we have built a team of individuals from a diverse range of backgrounds.

Are you ready to help power the global quality of life? Join Enverus, and be a part of creating a brighter, more sustainable tomorrow.

We are currently seeking a highly driven Senior Data Scientist based in Canada with experience or interest in power markets and congestion to join our data science team. This role offers the opportunity to work at a rapidly growing company delivering industry-leading solutions in one of the world’s most dynamic sectors.

You will be a key contributor to Enverus’ fastest-growing product line. The team pairs complex algorithms and machine learning with powerful computational infrastructure and an intuitive user interface that is unmatched in the industry. Small, fast-paced, and highly skilled, this group welcomes talented engineers from diverse backgrounds to help shape the future of energy together. This role works closely with engineering and product teams to deliver production-grade machine learning systems used directly by customers.

Performance Objectives

• Design, build, deploy and maintain machine learning models supporting core product capabilities
• Prototype, evaluate and productionize models using Python and modern ML frameworks
• Own individual models end-to-end, from development through monitoring and iteration
• Deploy models into cloud-native and containerized environments
• Build and maintain scalable training and inference workflows
• Monitor production model performance using metrics, alerts and dashboards
• Analyze model performance, design experiments and drive continuous improvement
• Perform and lead feature engineering on large, complex datasets from multiple sources
• Work with large datasets using SQL and analytical data tools
• Extend and maintain data ingestion and scraping platforms supporting model training and inference
• Collaborate closely with software engineering and product teams to align models with customer needs
• Participate in operational support related to data pipelines or production models, as needed.
• Participate in technical design discussions, code reviews and data science best practices
• Communicate findings, tradeoffs and recommendations clearly to both technical and non-technical stakeholders

Competitive Candidate Profile

• 5+ years of relevant industry or research experience in data science or machine learning
• Bachelor’s degree in a quantitative field such as Data Science, Computer Science, Mathematics, Software Engineering or related discipline
• Strong proficiency in Python, including experience with:
• • scikit-learn
  • PyTorch
• Solid understanding of machine learning fundamentals, including supervised and unsupervised learning, model evaluation and feature engineering
• Experience developing predictive models and taking them through the full model lifecycle
• Ability to write well-documented experiments and production-ready code
• Experience supporting models in production environments
• Familiarity with containerization and orchestration technologies
• Strong SQL skills
• Ability to develop reports and visualizations for internal teams

Desired Qualifications

• Familiarity with energy markets
• Experience with GCP and/or AWS
• Experience with time-series modeling or forecasting
• Experience working with streaming data and event-driven systems
• Experience with Snowflake, Databricks or similar cloud data warehouses
• Data engineering experience or experience building data pipelines
• Experience using infrastructure-as-code tools such as Terraform or Pulumi