Calidad jobs in United States

Wood Mackenzie is the global data and analytics business for the renewables, energy, and natural resources industries. Enhanced by technology. Enriched by human intelligence. In an ever-changing world, companies and governments need reliable and actionable insight to lead the transition to a sustainable future. That’s why we cover the entire supply chain with unparalleled breadth and depth, backed by over 50 years’ experience. Our team of over 2,400 experts, operating across 30 global locations, are enabling customers’ decisions through real-time analytics, consultancy, events and thought leadership. Together, we deliver the insight they need to separate risk from opportunity and make confident decisions when it matters most.

• Trusting – we choose to trust each other
• Customer committed – we put customers at the heart of our decisions
• Future Focused – we accelerate change
• Curious – we turn knowledge into action

Role Purpose

The Data Analyst will play a key role in advancing forecasting and modelling efforts by managing, analysing, and optimising large datasets across renewable, conventional, and storage assets globally. Working on asset revenue forecasting, power dispatch modelling, or power markets data, the analyst will focus on automating workflows, ensuring data quality, and delivering actionable insights through close collaboration with research, engineering, and product teams. This role contributes directly to improving forecasting accuracy and enhancing the company's global asset performance and revenue strategies.

Responsibilities

Knowledge

• Structures poorly defined problems, gathers feedback, and solves them effectively.
• Defines hypotheses and appropriate analysis approaches.
• Ensures statistical validity of results while avoiding common pitfalls.
• Develops deep technical expertise and emerging domain knowledge in forecasting or dispatch modelling.
• Provides specialist skills in data analysis, visualisation, and data management.
• Fully understands the technical data landscape within the energy domain.
• Maintains a growth mindset, continually evolving their skills.

Impact

• Performs defined and repeatable tasks with limited guidance.
• Independently formulates new analyses and project components.
• Carries out independent analysis on specific areas of forecasting or modelling projects.
• Delivers high-quality outputs on time, recognised for accuracy and reliability.
• Demonstrates strong proficiency in SQL or Python for data handling.
• Supports data governance and quality assurance activities.
• Engages directly with stakeholders, building confidence in data-driven outcomes.
• Works closely with peers in Research, Product, Data, Data Engineering, and other business functions.
• Identifies opportunities for efficiency, optimisation, or new data creation within their focus area.
• Uses technical and domain knowledge to create new value and insights.

Specialisms

General

• Owns and manages data within their role or team focus.
• Leverages specialist toolsets or domain expertise for data analysis and decision-making.
• Applies best practices in data modelling to support use cases.
• Collaborates with engineers to enhance data modelling capabilities for large-scale datasets.

Experience & Qualifications

• Bachelor’s degree in Data Science, Computer Science, Mathematics, or a related field.
• 2–4 years of experience in data analysis, including handling large, complex datasets.
• Advanced SQL skills for querying and managing relational databases.
• Familiarity with data visualisation tools (e.g., Sisense, Power BI, Streamlit).

Technical Skills

• Experience with ETL processes and APIs for data integration.
• Understanding of statistical methods and data modelling techniques.
• Familiarity with cloud platforms like Snowflake is advantageous.
• Knowledge of data governance frameworks and data security protocols.

Soft Skills

• Exceptional attention to detail and problem-solving capabilities.
• Strong communication skills, able to translate complex insights for non-technical stakeholders.
• Collaborative mindset with a commitment to continuous learning and staying updated on energy sector trends.

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