Wealth Management, Quantitative Portfolio Manager, Equities CIO, Vice President

We need to output HTML fragment with sections: About the role? Actually we need to use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must include only sections that have real specific content. The posting includes: Job Description (intro), Responsibilities (list), Required Responsibilities, Capabilities And Skills (list), Preferred Responsibilities, Capabilities And Skills (list), About Us (company description), About The Team (team description). There's no explicit Pay, Benefits, Schedule details. Benefits are mentioned in About Us: "We offer a competitive total rewards package... benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more." That's specific benefits content. So we can include a Benefits section with that list. Pay: they mention base salary determined based on role, experience, skill set and location; commission-based pay and/or discretionary incentive compensation, paid in cash and/or forfeitable equity. That's specific enough? It says "competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity". That's specific enough to include a Pay section. Schedule: not mentioned. So we omit Schedule. We need headings:

for each section. Use

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

  for prose. We must not repeat job title as heading. No Job ID. Remove boilerplate: EEO, equal opportunity, scam warning, etc. Also remove navigation, cookie, legal. Also remove duplicated lines. Also remove application instructions (none present). Also remove "About Us" maybe we keep as section? The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings like About Us or About The Team, but they said "use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)". It implies we should use those headings if content exists. Could also include About Us and About The Team as sections? Not in list but could be okay. However they said "Prefer a few substantive sections over many thin ones." Might combine About Us and About The Team into an "About the company" section? But we have specific content: About Us (company description) and About The Team (team description). Could combine into one section maybe "About the company and team". But safer to follow instruction: only use those headings if content exists. So we need to map content to those headings. We have: - About the role: The introductory paragraph describing role: "As a Quantitative Portfolio Manager, VP, within Wealth Management's Chief Investment Office (CIO) – Equities team, you will be a senior leader..." That's about the role. So we can put that under

  About the role

  with

  . - Responsibilities: The list under "Responsibilities". Use

  • . - Requirements: The "Required Responsibilities, Capabilities And Skills" list. That's requirements. So

    Requirements

    with
    . - Qualifications: Could be the "Preferred Responsibilities, Capabilities And Skills" list? That's more like preferred qualifications. Could treat as Qualifications. Or we could have Skills section for technical skills. But we have both required and preferred. We could put required under Requirements, preferred under Qualifications. Or we could put required under Requirements and preferred under Skills. The instruction includes Skills as a possible heading. Let's decide: Use Requirements for required, Qualifications for preferred (education, CFA). Use Skills for technical skills (Python, stats, ML). However the required list already includes programming capability etc. Might be overlapping. Better to follow typical structure: Requirements (experience, education), Qualifications (preferred), Skills (technical). But we have limited time. Let's extract: Required Responsibilities, Capabilities And Skills bullet points: - 6+ years of experience in quantitative investing, equity research, portfolio construction, or risk analytics (buy-side preferred), with demonstrated impact on portfolio outcomes (alpha, risk-adjusted returns, drawdown control, implementation efficiency). - Deep understanding of equity markets, factor investing, risk modeling, and portfolio construction under real-world constraints (turnover, costs, liquidity, client guidelines). - Proven experience owning or heavily influencing risk model usage (Axioma or similar), exposure management, scenario analysis, and attribution. - Advanced programming capability in Python, including strong applied experience with data analysis libraries (Pandas, NumPy, SciPy, stats/ML stack) and production-quality research practices (version control, testing, code review). - Solid grounding in statistics/econometrics and familiarity with ML techniques appropriate for investment contexts (regularization, tree-based methods, cross-validation, time-series pitfalls). - Bachelor's degree required; Preferred Responsibilities, Capabilities And Skills bullet points: - Master's/PhD in a quantitative discipline (Math, CS, Engineering, Statistics, Financial Engineering, etc.) strongly preferred. - CFA progress or designation is a plus (not required), particularly where it strengthens investment judgement and communication with fundamental stakeholders. Thus we can map: Requirements: the required list (including experience, understanding, risk model, programming, stats, bachelor's degree). Qualifications: the preferred list (Master's/PhD, CFA). Skills: maybe we could separate programming and stats as skills but they are already in required. Could still have Skills section for technical skills like Python, Pandas, NumPy, etc. But to avoid duplication, maybe we just have Requirements and Qualifications and Skills (technical) but we risk duplication. However instruction: "Prefer a few substantive sections over many thin ones." So maybe combine required and preferred into Requirements and Qualifications, and skip Skills. But we have technical details that could be under Skills. Let's think: The posting includes "Advanced programming capability in Python..." etc. That's a skill. Could be under Skills. The required list also includes experience and understanding. So we could split: Requirements: experience, education, understanding. Skills: programming, stats/ML. Qualifications: preferred education/CFA. Thus we need three sections: Requirements, Skills, Qualifications. Now Benefits: we have benefits list from About Us. Pay: we have compensation description. About the role: intro paragraph. Responsibilities: list. Now About Us and About The Team: we could incorporate into About the role? Not needed. Could omit as they are not in the heading list. But they are substantive. However instruction says "use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)". It doesn't forbid other headings, but they gave examples. Safer to stick to those headings only. So we omit About Us and About The Team? But that would lose substantive info about company and team. However maybe they consider that as boilerplate? It's not boilerplate; it's descriptive. But they didn't list "About the company" as a heading. Could we put that under About the role? Not appropriate. Could we create a section "About the company" using

    About the company

    ? The instruction didn't forbid other headings, just gave examples. It says "use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The "e.g." suggests those are examples, not exhaustive. So we can add other headings if needed. However they also say "Prefer a few substantive sections over many thin ones." Adding About Us and About The Team would increase sections. Could combine them into one "About the company and team" section. Let's create a section "About the company" with combined description from About Us and About The Team. That's substantive. Thus sections: - About the role - Responsibilities - Requirements - Skills - Qualifications - Benefits - Pay - About the company (optional) We need to ensure we don't create sections with filler like "commensurate with experience". We have real content. Now craft HTML. We'll need to escape any HTML special characters? Not needed. Let's write:

    About the role

    As a Quantitative Portfolio Manager, VP, within Wealth Management's Chief Investment Office (CIO) – Equities team, you will be a senior leader in a growing, innovative Equity Portfolio Management organization, reporting to the Head of Equities. You will set the quantitative research agenda, own core portfolio analytics and risk frameworks, and drive implementation of systematic, factor-based and data-driven insights for an $80bn equity portfolio benchmarked against MSCI World. This role requires deep expertise in equity factor research, portfolio construction, and risk management—combined with the credibility to influence other senior portfolio managers and fundamental analysts. You will translate complex quantitative work into investment decisions, elevate the team's analytical capabilities, and serve as a thought partner to CIO leadership on process, tooling, governance, and portfolio outcomes.

    Responsibilities

    • Act as the senior quantitative partner to the equity team, influencing security selection overlays, factor tilts, risk budgeting, and implementation choices across regional and global mandates.
    • Lead the integration of quantitative signals with fundamental views, ensuring a repeatable, well-governed investment process.
    • Own the application and interpretation of multi-factor risk models (e.g., Axioma and/or equivalent) to monitor exposures, crowding, concentration, liquidity considerations, and scenario sensitivities.
    • Establish escalation frameworks and decision support for material risks; contribute to portfolio review cadence and senior risk discussions.
    • Design and improve portfolio construction frameworks including constraints, turnover control, transaction cost awareness, and rebalancing discipline.
    • Lead performance attribution and factor decomposition to diagnose drivers of returns, active risk, and drawdowns; turn findings into actionable portfolio recommendations.
    • Drive development of scalable research and analytics tooling (Python-first), including data pipelines, reusable libraries, and standardized reporting for PM workflows.
    • Evaluate and apply machine learning/AI techniques where appropriate (feature engineering, ensemble methods, NLP for alternative data), with emphasis on interpretability and investment relevance.
    • Communicate complex quantitative concepts clearly to senior investment professionals; deliver crisp trade-offs and recommendations rather than “model outputs.”
    • Partner with technology, data, risk, compliance, and control stakeholders to ensure model governance and appropriate use.
    • Maintain a consistent focus on risk management, model governance, suitability, and adherence to applicable policies and controls.

    Requirements

    • 6+ years of experience in quantitative investing, equity research, portfolio construction, or risk analytics (buy-side preferred), with demonstrated impact on portfolio outcomes (alpha, risk-adjusted returns, drawdown control, implementation efficiency).
    • Deep understanding of equity markets, factor investing, risk modeling, and portfolio construction under real-world constraints (turnover, costs, liquidity, client guidelines).
    • Proven experience owning or heavily influencing risk model usage (Axioma or similar), exposure management, scenario analysis, and attribution.
    • Bachelor's degree required.

    Skills

    • Advanced programming capability in Python, including strong applied experience with data analysis libraries (Pandas, NumPy, SciPy, stats/ML stack) and production-quality research practices (version control, testing, code review).
    • Solid grounding in statistics/econometrics and familiarity with ML techniques appropriate for investment contexts (regularization, tree-based methods, cross-validation, time-series pitfalls).

    Qualifications