BI Analyst - Renewable Energy
Royal Dutch Shell
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About the Role
Are you a renewable industry professional with a passion for data? Are you motivated to apply your technical knowledge and experience in renewables space to help make commercial decisions? Are you aspiring to build a career in data science solving challenging problems associated with energy transition? If your answer is yes, read on.
NOTE: This role within Finance Operations Commercial Data Science team is best suited for renewable industry professionals who are motivated to transition to a data science career. Relevant experience in renewable energy domain is a must. Strong data analysis skills are required. Exposure to data science via coursework, certification or volunteer projects would be an advantage. Prior data science experience as such is not required. Employees in this role would be provided guidance and support to transition into data science roles in a structured manner as appropriate.
This is a non-MCDS skill-pool role with Commercial Data Science team.
General background on position
- This position is part of Shell’s growing Finance & Data Operations Commercial Data Science Team.
- With 200+ data scientists based primarily in Chennai and Krakow, Commercial Data Science team is a key digitalization partner to Shell’s Upstream, Integrated Gas, Renewables and Downstream business units.
- This team delivers fit for purpose data science solutions from proof-of-concept to deployment for Shell’s global assets and provide tangible commercial value to business units within Shell by enabling data-driven decision making.
- The analyst will join a high growth global data science team to support senior stakeholders with data driven commercial decision making in renewable energy business.
Key responsibilities of the role
- Analyze diverse data sets related to renewables business and deliver customer recommendations for renewable energy solutions.
- Perform techno-economic modelling rapidly to screen onsite energy generation and storage opportunities
- Collaborate effectively with stakeholders and experts from various geographies and disciplines.
- Articulate insights and recommendations from data analysis in a business-friendly language using impactful visualization techniques.
Requirements
Education & Experience
- Strong background in quantitative skills gained from Tier 1 institutes
- Master’s degree in science /business/economics/ quantitative finance OR
- Engineering with strong experience in solving business problems by statistical or mathematical approach.
- 3+ years of relevant experience in renewable energy domain is a must
Industry expertise
Experience in renewable energy space with exposure to any one or more of the following functional areas:
- Engineering & design of renewable energy solutions, energy efficiency measures
- Techno-economic modelling: optimization of renewable energy asset configuration based on defined economic parameters, scenario, and sensitivity analysis
- Business development, solutions sales & marketing
- Skills in using relevant tools such as Helioscope, PVsyst, Homer Grid, Energy Toolbase, PowerBI, MS Excel would be advantageous
Analytical Skills
- Passion for data with strong analytical mindset and focus on drilling data for commercial insight
- Demonstrated ability to work with large and diverse data sets
- Learner mindset with demonstrated ability to identify skill/experience gaps, take proactive steps to close them and quickly deploy skills the from new learning to business problems.
Stakeholder Management Skills
- Have an entrepreneurial approach towards helping a growing pool of stakeholders understand value from data science approaches for renewables business.
- Fluent in communicating (verbal and written) with other data science team members and global stakeholders who could be from diverse engineering and business backgrounds
- Ability to articulate the assumptions, risks, and upsides of the analysis recommendations
- Understand business KPI's, frameworks and drivers for performance
- Remain engaged with stakeholders and be resilient in the face of delays, setbacks, and ambiguity
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