Energy Acuity
Senior Data Scientist - 25459 2 Locations
Energy Acuity, Denver, Colorado, United States, 80285
Senior Data Scientist
Join the team at Energy Acuity as a Senior Data Scientist.
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. Our solutions are reshaping how energy is consumed and managed, offering anytime‑anywhere access to analytics and insights that help customers make better decisions for clean, affordable energy.
The energy industry is changing fast, yet we continue to lead through intelligent connections across the entire energy ecosystem—from renewables, power and utilities, to oil and gas and financial institutions. Our solutions improve efficiency, capital allocation, renewable energy development, investment, and sourcing, while reducing costs by automating critical business operations. Our success is built on a diverse team of talented professionals.
Are you ready to help power the global quality of life? Join Enverus and be 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 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, trade-offs 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
#J-18808-Ljbffr
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. Our solutions are reshaping how energy is consumed and managed, offering anytime‑anywhere access to analytics and insights that help customers make better decisions for clean, affordable energy.
The energy industry is changing fast, yet we continue to lead through intelligent connections across the entire energy ecosystem—from renewables, power and utilities, to oil and gas and financial institutions. Our solutions improve efficiency, capital allocation, renewable energy development, investment, and sourcing, while reducing costs by automating critical business operations. Our success is built on a diverse team of talented professionals.
Are you ready to help power the global quality of life? Join Enverus and be 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 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, trade-offs 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
#J-18808-Ljbffr