Job Family: Research Job Series: Social Science Research Professional Job Code: 4188 Grade: H Exemption: Exempt
1-year Fixed-term Position
Sustainable Finance Initiative Research Associate: Sustainability Risk Analytics and Financial Products Stanford's Sustainability Finance Initiative (SFI), working in partnership with the Climate Policy Initiative (CPI), seeks a talented, highly motivated researcher with a background in data analytics and machine learning techniques for analysis and forecasting of potential climate mitigation actions. The forecasts will be essential components in the development of innovative approaches to modelling global mitigation scenarios and probabilities, which in turn are a core component for developing new financial models and products for measuring and managing the physical and transitional risks (policy; technology) of a climate driven economic transition.
Problem background and project structure Around the world, national climate mitigation strategies and the international negotiations on climate change policy and investment have been driven by a set of scenarios that are deterministic in nature. These scenarios, including widely used scenarios issued by respected agencies such as the International Energy Agency (IEA) or the United Nations Framework Convention on Climate Change (UNFCCC), typically prescribe a mix of technology, investment, and behaviour change that will be needed to meet global climate goals, and assume that policy and markets will make these investments and changes happen. Some of these scenarios attempt to incorporate relative costs and political barriers to the actions, but fail to incorporate the probabilities that technology costs, investments, or political barriers might shift dramatically in the future.
For the purposes of policy development, the current scenarios identify important directions to encourage investment and technology development. However, for financial markets seeking to address climate transition risk, they provide only a single static picture of a potential future, which is likely to be wide of the mark. Even for policy makers they are insufficient, as these scenarios tell us little about how policies interact with each other and how sets of policies can be developed as contingencies if other policies are more or less successful than planned.
Investors, and policy makers, thus need to go beyond assessing the probability that a particular transition (well below 2 degrees C?) will occur, to assess what is the probability different mixes of technologies and investments will occur - including both complete and partial transitions.
The Stanford Sustainable Finance Initiative-- working with CPI, a coalition of central banks and development banks, and a group of forward-looking industrial companies and financial institutions--, are developing a new process for creating probabilistic, technology and data based, adaptive scenarios to improve risk management, investment decisions, and policy making. These scenarios decompose climate mitigation scenarios Into a series of approximately 20 technology development and deployment, investment and behavioural actions that determine the future path of carbon emissions. The aim is to assess the probability of each action achieving different levels of greenhouse gas reductions over the next 5 to 40 years. We will then integrate these scenarios to assess different, more probable mixes of actions and then evaluate the impact on the global economy and specific investments.
The immediate, and most significant, phase of this work is to use machine learning, data based forecasts for each of the key variables of the scenarios including concepts such as CCS deployment, electrification of transport, electrification of industry, decarbonization of electricity supply (including renewable energy penetration), energy productivity (units of energy per unit GDP as a proxy for energy efficiency), biofuel development, land use change, coal to gas switching, and a series of similar variables.
Mentorship structure The project will be based at Stanford University, in collaboration with the Climate Policy Initiative Energy Finance team headquartered in London, UK. Professor Thomas Heller, Professor of International Policy Studies (emeritus) and the Director of the Sustainable Finance Initiative, and David Nelson (CPI-EF Director) will directly supervise the project, in consultation with Stanford faculty from applied computer science and associated international public and corporate risk research analysts. Essential Qualifications SFI is looking for data scientists and machine learning experts who would be interested in applying their skills to the development of machine learning models to a set of these variables, using techniques such as agent-based behavioural specification, multi-variate causal relationships, interactive learning, non-equilibrium or evolutionary dynamics, endogenous probability (as opposed to politically mandated objectives [e.g. two degree climate scenarios], and data driven weak correlation discovery.
The SFI Research Associate will work on a set of specific variables, but also work within a global team that includes representatives and analysts from across the spectrum of institutions described above. In particular, there will be the opportunity to contribute in integrating these individual analyses into the new concept for model development.
A focus on using data analytics and machine learning to develop forward looking forecasts;
Interest in applying computer science techniques to technology development, economics, or social sciences analysis;
An insightful, independent, analytical mind with a strong track record of best-in-class analysis and critical thinking that has had a measurable impact;
Demonstrable ability to influence key stakeholders at the highest level;
Excellent verbal and written communication skills to articulate methods, insights and recommendations from complex qualitative and quantitative analysis in a compelling and influential way both internally and to expert and non-technical audiences;
Experience of influential engagement with executive-level engagement, comfort working alongside high ranking government officials, leaders in finance and industry, as well as academic experts and research organizations;
Professionalism and enthusiasm for working with a diverse team in a collegiate and productive manner;
All interested and qualified applicants must follow these steps in order to apply: All applicants must apply via the Stanford Careers website http://stanfordcareers.stanford.edu/job-search. Please search by selecting Precourt Institute for Energy' in the Location' search field. Please submit the following materials:
A cover letter;
A curriculum vitae;
List of three references.
Note: Not all unique aspects of the job are covered by this job description
JOB PURPOSE: Perform work to support research or develop public programs and policies requiring a high degree of scientific knowledge. Supervise and train less experienced colleagues and provide consultation.
Assist principal investigators with designing experiments constituting a phase of a research project for which precedents are few; conduct experiments which may involve coordinating a complex experimental protocol.
Independently identify/modify standard procedures based on judgment concerning the general goals of a project; select approaches in developing, testing, and evaluating new methods.
Lead or participate in multidisciplinary teams (in academia, across different faculties or schools, and in industry, across different functions of the business).
Co-author all or sections of a report for publication or professional presentation with the work being reviewed by the principal investigator or faculty.
Present ongoing work and findings to colleagues at academic conferences.
Collaborate with the principal investigator to design a research approach of a project and/or integrate results of the entire project.
Complete project-related administrative and budgetary responsibilities of a limited scope as needed.
Formally supervise and train new staff or students, including hiring, performance management, and related duties, in addition to instruction on techniques and consultation on project work.
- Other duties may also be assigned
Education & Experience: Graduate degree in an applicable social science related field and five years of applicable experience, or combination of education and relevant experience in an applicable social science. Knowledge, Skills and Abilities:
Expert level knowledge and skills in field of science related to research project.
General computer skills and ability to quickly learn and master computer programs.
Strong analytical skills and excellent judgment.
Ability to work under deadlines with general guidance.
Excellent organizational skills and demonstrated ability to complete detailed work accurately.
Demonstrated oral and written communication skills.
Ability to work with human study participants.
Certifications and Licenses: None
Frequently perform desk-based computer tasks, grasp lightly/fine manipulation, lift/carry/push/pull objects that weigh up to 10 pounds.
Occasionally stand/walk, sit, use a telephone, writing by hand, and sort/file paperwork or parts.
- Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.
May be required to work non-standard, extended or weekend hours in support of research work.
Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu.
Location: Dean of Research, California, United States Schedule: Classification Level:
Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.
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