PM Engagement Analyst

Verition Group LLC

$80K — $120K *
Miami, FL 33186In-Person
Finance & Insurance
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 2-4 years of experience in financial services, supporting investment teams and portfolio analytics.
  • Proficiency in Python and SQL for data manipulation.
  • Strong skills in handling and analyzing large datasets.
  • Understanding of Long/Short Equity investing principles.
  • Familiarity with factor models and portfolio risk concepts.
  • Demonstrated curiosity and strong judgment in analysis.
  • Ability to communicate analysis clearly to diverse audiences.

Responsibilities

  • Collaborate with senior L/S engagement team on data analysis for PM performance evaluation.
  • Assist in portfolio diagnostics and development of research tools.
  • Utilize factor models to provide insights into portfolio risk and performance drivers.
  • Conduct analysis on portfolio positioning and alpha signals across strategies.
  • Support risk decomposition and factor attribution processes.
  • Employ Python and SQL to automate workflows and build analytical tools.
  • Enhance internal data workflows and reporting for investment teams.
  • Drive the adoption of internal analytical tools and enhance research capabilities.

Benefits

  • Collaborative working environment with front-office investment professionals.
  • Opportunity to contribute to cutting-edge data and analytics workflows.
  • Exposure to high-level decision-making and investment strategies in a multi-strategy hedge fund.
  • Potential for growth and development within a leading financial firm.
  • Involvement in innovative projects leveraging AI and modern data tooling.
Full Job Description
We are seeking a highly motivated PM Engagement Analyst to support the senior PM Engagement team in their work with Portfolio Managers and analysts within our Long/Short Equity business at a leading multi-strategy hedge fund. This role sits at the intersection of investing, data, analytics, and technology, helping teams leverage data, factor analytics, and internal tools to improve research workflows and investment decision-making. The ideal candidate will have a strong analytical mindset, foundational technical skills in Python and SQL, familiarity with factor models and equity investing, and a desire to work closely with front-office investment professionals in a fast-paced environment.

Key Responsibilities
  • Partner directly with senior members of the L/S engagement team to support data-driven workflows analyzing L/S PM performance and trading behavior.
  • Support engagement team on ad hoc analysis, portfolio diagnostics, and research tooling.
  • Work with factor models and portfolio analytics frameworks to help investment teams better understand exposures, risk, and performance drivers.
  • Analyze portfolio positioning, style exposures, and alpha signals across L/S Equity strategies.
  • Support workflows related to risk decomposition, factor attribution, and portfolio construction.
  • Use Python and SQL to analyze large datasets, automate workflows, and build lightweight analytical tools.
  • Help improve internal data workflows and reporting processes for investment teams.
  • Work closely with data teams and quant researchers, to identify gaps in workflows and improve the investment process.
  • Contribute to the rollout and adoption of internal analytics and research tools.

Required Qualifications
  • 2-4 years of experience within financial services, ideally supporting investment teams, research workflows, data analysis, or portfolio analytics.
  • Foundational proficiency in Python and SQL
  • Comfortable working with large datasets and performing data analysis
  • Understanding of fundamental Long/Short Equity investing
  • Familiarity with factor models, portfolio analytics, and risk concepts
  • Strong judgment and intellectual curiosity
  • Ability to not just produce analysis but also present it in a clear and engaging manner

Desirable Skills
  • Experience with portfolio/risk platforms such as Barra, Axioma, FactSet, Bloomberg, or similar tools.
  • Exposure to alternative data workflows and equity research processes.
  • Familiarity with visualization and analytics tools (Plotly, Streamlit, Dash).
  • Interest in AI-driven research workflows and modern data tooling.

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