Blackstone is seeking a Vice President within BXDS to focus on data science and investment analytics for Private Equity Secondaries. This individual will work closely with Blackstone’s secondaries investment professionals to develop analytical models, pricing frameworks, data infrastructure, and AI-enabled workflows that support more scalable and differentiated investment decision-making.

The ideal candidate will combine strong foundational data science and quantitative modeling expertise with commercial judgment, investment curiosity, and operate independently in a fast-paced, stakeholder-driven environment. This candidate should be comfortable moving across the full data science lifecycle: understanding investment problems, sourcing and structuring data, developing models, building repeatable workflows, communicating insights to business stakeholders, and helping drive adoption.

This role will focus heavily on modeling and analytics for secondaries, including pricing, portfolio company analytics, cash flow and NAV forecasting, benchmarking, scenario analysis, and AI/LLM-enabled workflows for processing and reasoning over complex investment materials. The candidate should bring new ideas to how data science can improve secondaries investing while also being hands-on enough to strengthen the underlying data and analytical infrastructure required to scale those ideas.

This is a high-impact role for someone who can drive projects end-to-end, partner effectively with senior investment stakeholders, and build practical, interpretable, and scalable solutions that directly support the investment process and scale across one of the firm’s most dynamic investment strategies.

Key Responsibilities

·      Lead the development of data science models and analytical tools to support PE secondaries investment decision-making, with a focus on pricing, valuation, portfolio analytics, and underwriting.

·      Partner closely with investment professionals to understand business needs, frame analytical questions, and translate them into practical data-driven solutions.

·      Develop repeatable workflows that help the investment team evaluate opportunities more efficiently and consistently.

·      Identify opportunities to use new datasets, modeling techniques, and AI/LLM tools to improve investment process.

·      Build AI-enabled workflows to extract, summarize, and analyze information from investment materials such as GP reports, fund documents, financial statements, CIMs, and deal memos.

·      Work with data engineering and technology partners to improve data pipelines, data quality, and the infrastructure supporting secondaries analytics.

·      Drive projects independently from initial problem definition through execution, delivery, and adoption.

Relevant Profile

·      5+ years of experience in data science, quantitative analytics, investment analytics, machine learning, or a related data-driven role.

·      Strong technical skills in Python and SQL, with experience working with complex and unstructured datasets.

·      Experience building analytical models, forecasting tools, or decision-support systems in a business-facing environment.

·      Strong foundation in statistics, predictive modeling, and quantitative methods.

·      Experience working with financial, investment, transaction, fund-level, or portfolio company data.

·      Familiarity with private markets, private equity, secondaries, asset management, fintech, or other investment-oriented domains is preferred.

·      Practical experience with AI/LLM workflows, including document extraction, summarization, data normalization, or knowledge retrieval.

·      Work across both modeling and data infrastructure, including data pipelines, data quality, and scalable analytical workflows.

·      Strong communication skills, with the capability to explain technical concepts and model results to non-technical audiences.

·      Effective relationship builder with strong stakeholder management and influencing skills.

·      Self-starter who can independently manage projects, balance priorities, and deliver high-quality work with limited oversight.

·      Bachelor’s degree in a quantitative field such as computer science, data science, statistics, or a related discipline; advanced degree preferred but not required.

The duties and responsibilities described here are not exhaustive and additional assignments, duties, or responsibilities may be required of this position.  Assignments, duties, and responsibilities may be changed at any time, with or without notice, by Blackstone in its sole discretion.

Expected annual base salary range:

Actual base salary within that range will be determined by several components including but not limited to the individual's experience, skills, qualifications and job location. For roles located outside of the US, please disregard the posted salary bands as these roles will follow a separate compensation process based on local market comparables.

Additional compensation and benefits offered in connection with the roleconsist of comprehensive health benefits, including but not limited to medical, dental, vision, and FSA benefits; paid time off; life insurance; 401(k) plan; and discretionary bonuses. Certain employees may also be eligible for equity and other incentive compensation at Blackstone’s sole discretion.