Estimation Methodologies Lead - ESG Research

at  MSCI Inc

RESEARCH

Use your eye for actionable insights to help clients identify and manage emerging risks and opportunities.

SPECIFIC SKILL SETS:

• Advanced data science and programing skills (e.g. Python), focusing on data analysis, model development, and automation are essential; experience in implementing advanced estimation models is preferred.
• Experience in leveraging statistical techniques and machine learning models to accurately assess and forecast the economic and environmental impacts of corporate activities.
• Proven ability to handle and analyze company-level data, including financial statements, operational metrics, and economic and external market data.
• Strong analytical and quantitative skills with previous experience in data manipulation and quantitative model development.
• Excellent verbal and written communication skills to maintain effective relationships with a global team of industry research analysts.
• High motivation to join a dedicated, technically proficient, and enthusiastic team.

DESIRED EXPERIENCE AND QUALIFICATIONS:

• A minimum of 5 years of full-time professional experience in a quantitative or economic research role, with prior exposure to capital markets preferred.
• Bachelors’ degree required; an advanced degree in a related field (Quantitative finance, computer science, engineering, econometrics, mathematics) is preferred.
• Substantial experience in the financial sector, risk management, or a closely related field, with a deep understanding of economic exposure and its impact on companies, is preferred.
• While experience with ESG analysis is not a pre-requisite, candidates should demonstrate a keen interest in the integration of ESG issues into investment decisions.

YOUR TEAM RESPONSIBILITIES

MSCI ESG Research is seeking a Head of Estimation Methodologies to join our ESG Investment Models & Analytics team. This role involves developing sophisticated models to estimate companies’ economic exposures to various business activities, leveraging company-level data and applying large language models. We are looking for a candidate who is passionate about using data science to enhance understanding and decision-making in economic exposure analysis. The selected applicant will engage in R&D and methodology projects that aid investors in integrating ESG risks in their fundamental research, portfolio construction, and risk management processes. This role involves applying estimation methodologies across multiple ESG models, aiding investors in evaluating company sustainability risks and sustainable impacts. The model applications include, but are not limited to, estimating revenues for companies in fossil fuel and other climate-negative industries; assessing the environmental impacts of various business operations; utilizing generative AI to identify and evaluate potential risks and opportunities of emerging technologies across different sectors; and effectively developing sustainability models to compare the sustainability performance of companies within the same industry, especially with limited disclosures.

YOUR KEY RESPONSIBILITIES

• Model Development: Design, develop, and refine models that accurately estimate companies’ economic exposures to specific business activities, ensuring the models are robust, scalable, and efficient.
• Data Analysis: Perform comprehensive data analysis to understand company-level and macro-level data, utilizing this insight to construct predictive models. This involves data collection, cleaning, and preprocessing to ensure high-quality inputs for model development.
• Collaboration and Communication: Collaborate with the research, data, analytical quality assurance, and application development teams, as well as stakeholders across the organization to define modeling objectives, share insights, and present outcomes. Clearly and effectively communicate complex model mechanisms and results to both technical and non-technical audiences.
• Innovation and Research: Lead research into new modeling techniques and technologies, including large language models, to maintain the organization’s leading edge in the economic estimation methodologies.