Job Description

Responsibilities Be a bridge between business and digital organizations and part of a cross-disciplinary team, working closely with other analysts, product managers, data scientists, data engineers, software engineers, data managers and business partners. Provide actionable, data-driven business and product insights by combining deep statistical skills, data manipulation capabilities and business acumen. Maintain metrics and build dashboards. Autonomously execute data analysis. You apply existing data analytics strategies relevant to your immediate scope You partner with data engineers to define and build simple data models. You integrate existing tools to automate data ingestion, data manipulation, quality control and data analysis. Adhere to and advocate for data analytics best practices. Present results to peers and team members Collaborates with stakeholders Mentor others - Working knowledge of data science to aid data analysis - Knowledge of LLMs and putting them into practice for data analysis Qualifications Essential: MSc in a quantitative field, preferably statistics. Hands-on experience (typically approx 5-7 years) carrying out data analytics, data mining and product analytics in complex, fast-paced environments. Applied knowledge of data analytics and data pipelining tools and approaches across all data lifecycle stages. Deep understanding of a few and a high-level understanding of several commonly available statistics approaches Advanced SQL knowledge Advanced scripting experience in R or python. Ability to write and maintain moderately complex data pipelines Strong business acumen. Customer-centric and pragmatic mindset. Focus on value delivery and swift execution, while maintaining attention to detail. Strong communication and stakeholder management skills. Ability to lead large organizations through influence. Continuous learning and improvement mindset. Desired Advanced analytics degree Experience with big data technologies (e.g. Hadoop, Hive, and Spark) is a plus. No prior experience in the energy industry required. Skills: Commercial Acumen, Communication, Data Analysis, Data cleansing and transformation, Data domain knowledge, Data Integration, Data Management, Data Manipulation, Data Sourcing, Data strategy and governance, Data Structures and Algorithms (Inactive), Data visualization and interpretation, Digital Security, Extract, transform and load, Group Problem Solving