Research Director, AI Quantitative Modeling

We need to output HTML fragment with sections: About the role? The posting includes description before responsibilities. We'll create sections: About the role (intro paragraph), Responsibilities (list), Required Qualifications (list), Preferred Qualifications (list), Why This Role Stands Out (maybe as a section? Could be Benefits? Actually it's more about company culture; could be a section like "About IDC" but not required. We can include as a section "Why This Role Stands Out" as prose. Also Compensation Transparency: we have pay range; we should include Pay section with that info. Also maybe Benefits? Not mentioned. Schedule? Not mentioned. So include Pay section. We must not repeat job title as heading. No Job ID. Remove boilerplate like EEO, etc. Also remove "#Remote #Quant". Also remove "Equal Opportunity Employer" etc. Also remove "Employment eligibility verification required." etc. Also remove list of states? That's part of remote eligibility; maybe we can keep as part of About the role? It's metadata but could be considered minor; we can drop or fold into short p. The instruction: do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it. The list of states is minor metadata; we could drop it. Probably better to drop. We need to preserve all substantive information. So we need to keep compensation range. Also keep "Compensation Transparency" paragraph. We need to use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. We need to include sections only when posting has real specific content. So we will have:

  About the role

  ... intro paragraph ... maybe also mention remote eligibility? Could include short p about remote work eligibility states? Might be considered minor; but we could include as part of About the role. However it's a list of states; maybe we can drop. The instruction: "fold minor metadata into a short

  or drop it." So we could include a short p about remote eligibility: "IDC currently employs remote workers in the following states: ..." That's a list; could be a

  but it's metadata. Probably better to drop to keep concise. But we need to preserve ALL substantive information; is remote eligibility substantive? It's about where they can hire; maybe considered substantive. However it's a list of many states; could be considered metadata but still relevant. I'd include a short p: "IDC currently employs remote workers in the following states: AZ, CA, CO, CT, DC, FL, GA, IL, IN, KS, MA, MD, ME, MI, MN, MO, MS, NC, NH, NJ, NY, OH, OR, PA, RI, SC." That's a short p. Now sections: About the role: intro paragraph (first paragraph up to "This is a hands-on quantitative research role.") plus maybe the remote eligibility p. Responsibilities: list each bullet from "Responsibilities" section. We need to capture all bullet points. The responsibilities paragraph includes many sentences; we need to break into list items. Let's extract: - Design and develop models of complex systems with multiple interacting variables, nonlinear relationships and changing conditions. - Frame research questions, select appropriate methods, and make assumptions, dependencies and limitations explicit. - Build AI cost models covering development, deployment and operation. Separate initial investment from recurring costs, and examine how workload, scale, utilization, technology choices and operating requirements affect total and unit costs over time. - Develop models of AI’s economic value, including productivity, cost savings and revenue effects. Define baselines, account for adoption and implementation costs, and avoid double counting. Distinguish capacity released from cash savings and forecast benefits from measured results. - Create forecasts, simulations and scenarios that capture uncertainty and interactions between drivers. Use sensitivity analysis and stress testing to identify which assumptions matter most and where conclusions become unreliable. - Identify, assess and combine data from IDC research and other relevant sources. Work with subject matter experts to establish defensible inputs, address gaps and separate observed evidence from assumptions. - Calibrate and validate models against available evidence. Test predictive performance where feasible, compare alternative approaches, investigate errors, and update models as new data becomes available. - Build reusable, documented research code and workflows with appropriate tests and version control. - Use agentic AI to accelerate coding, analysis and experimentation while independently checking the resulting code, methods and conclusions. - Explain methodologies and findings clearly to research colleagues and, when needed, clients. - Contribute quantitative analysis to IDC research outputs and extend the team’s modeling capability into subsequent projects. That's a lot; we can keep as list. Required Qualifications: list bullet points. - Advanced quantitative training in mathematics, physics, statistics, operations research, or a closely related discipline, with a strong foundation in probability, statistical inference and numerical methods. - A track record of independently developing and validating financial, scientific or other quantitative models of complex systems. Experience should demonstrate how models were used to support forecasts, decisions or practical research outcomes. - Ability to choose and apply appropriate forecasting, simulation or optimization methods, explain their assumptions, and work with incomplete or noisy data without overstating the precision of results. - Strong scientific programming skills, preferably in Python, and experience translating mathematical ideas into working, testable code. - Ability to develop reproducible analyses and work confidently with numerical libraries and structured data. - Current, regular use of agentic AI tools such as OpenAI Codex, Anthropic Claude Code, or comparable systems in their current role or daily life. Candidates must be able to discuss concrete workflows and explain how they verify generated code and analytical outputs. - Ability to learn unfamiliar technical and economic domains, challenge assumptions constructively, and work with subject matter experts to connect models with the systems they represent. - Clear written and verbal communication, including the ability to explain model logic, uncertainty and limitations to colleagues who do not share your mathematical background. Preferred Qualifications: list bullet points. - A PhD in mathematics, physics, statistics, operations research, or another relevant quantitative field is strongly preferred. Equivalent depth of research expertise demonstrated through applied work will also be considered. - Experience in quantitative research or model development at a hedge fund, investment bank, asset manager, or a comparable analytical environment. - Relevant backgrounds also include scientific research and industrial modeling. - Experience developing models of costs, investment returns, productivity or economic impact, or turning research methods into reusable analytical tools. - Deep prior expertise in the technology industry or a particular customer sector is not required. - Intellectual curiosity and the ability to learn the economics and technical characteristics of AI are essential. Why This Role Stands Out: paragraph(s). We'll put as

  Why This Role Stands Out

  ...

  Compensation Transparency: we need Pay section. Use

  Pay

  The expected total annual compensation, depending on location and experience, is between $150,000-$190,000, inclusive of base salary and variable compensation.

  Also maybe include the earlier paragraph about fair pay practices; but that's more generic; we could include as part of Pay section prose. The instruction: include Pay section only if posting actually states them. It does state compensation range. So we include Pay. We should not include Benefits or Schedule sections as not present. We must output only HTML fragment, no markdown fences. Let's craft. Check for any boilerplate removal: Remove "Equal Opportunity Employer IDC is committed to providing equal employment opportunities for all qualified persons. Employment eligibility verification required. We participate in E-Verify." Also remove "#Remote #Quant". Also remove the "Compensation Transparency At IDC, we are committed to fair and equitable pay practices..." we kept the range but maybe also keep the first sentence about fair pay practices? It's somewhat generic but could be considered part of Pay section. Might keep as part of Pay section prose: "At IDC, we are committed to fair and equitable pay practices. Employees are compensated equitably for their work, aligned with their skills and experience. Salary and incentive structures are determined through a rigorous process that considers experience, education, certifications, role-specific requirements, internal equity, and verified U.S. market data from an independent third-party partner. The expected total annual compensation, depending on location and experience, is between $150,000-$190,000, inclusive of base salary and variable compensation." That's substantive; we can include. Now produce HTML. Let's write:

  About the role

  IDC is seeking a Research Director to develop the quantitative models that underpin our AI Economics research. The initial focus will be on modeling the cost and economic value of AI with scope to contribute to a broader range of modeling and forecasting projects as the team’s research develops. This is a hands-on quantitative research role. You will translate complex questions into mathematical models, implement and test them in code, and develop methods that colleagues can reuse. Most of your time will be spent on model development, empirical analysis and validation, working with IDC’s industry experts. You will also contribute to selected client discussions and presentations where your technical expertise is needed.

  IDC currently employs remote workers in the following states: AZ, CA, CO, CT, DC, FL, GA, IL, IN, KS, MA, MD, ME, MI, MN, MO, MS, NC, NH, NJ, NY, OH, OR, PA, RI, SC.

  Responsibilities

  • Design and develop models of complex systems with multiple interacting variables, nonlinear relationships and changing conditions.
  • Frame research questions, select appropriate methods, and make assumptions, dependencies and limitations explicit.
  • Build AI cost models covering development, deployment and operation. Separate initial investment from recurring costs, and examine how workload, scale, utilization, technology choices and operating requirements affect total and unit costs over time.
  • Develop models of AI’s economic value, including productivity, cost savings and revenue effects. Define baselines, account for adoption and implementation costs, and avoid double counting. Distinguish capacity released from cash savings and forecast benefits from measured results.
  • Create forecasts, simulations and scenarios that capture uncertainty and interactions between drivers. Use sensitivity analysis and stress testing to identify which assumptions matter most and where conclusions become unreliable.
  • Identify, assess and combine data from IDC research and other relevant sources. Work with subject matter experts to establish defensible inputs, address gaps and separate observed evidence from assumptions.
  • Calibrate and validate models against available evidence. Test predictive performance where feasible, compare alternative approaches, investigate errors, and update models as new data becomes available.
  • Build reusable, documented research code and workflows with appropriate tests and version control.
  • Use agentic AI to accelerate coding, analysis and experimentation while independently checking the resulting code, methods and conclusions.
  • Explain methodologies and findings clearly to research colleagues and, when needed, clients.
  • Contribute quantitative analysis to IDC research outputs and extend the team’s modeling capability into subsequent projects.

  Required Qualifications

  • Advanced quantitative training in mathematics, physics, statistics, operations research, or a closely related discipline, with a strong foundation in probability, statistical inference and numerical methods.
  • A track record of independently developing and validating financial, scientific or other quantitative models of complex systems. Experience should demonstrate how models were used to support forecasts, decisions or practical research outcomes.
  • Ability to choose and apply appropriate forecasting, simulation or optimization methods, explain their assumptions, and work with incomplete or noisy data without overstating the precision of results.
  • Strong scientific programming skills, preferably in Python, and experience translating mathematical ideas into working, testable code.
  • Ability to develop reproducible analyses and work confidently with numerical libraries and structured data.
  • Current, regular use of agentic AI tools such as OpenAI Codex, Anthropic Claude Code, or comparable systems in their current role or daily life. Candidates must be able to discuss concrete workflows and explain how they verify generated code and analytical outputs.