Data Scientist – Investment Analytics

Job Description

Data Scientist | The Hollister Group

The Tone:
This is a full-time hybrid role for The Hollister Group’s client, located in NYC. The client integrates data science and machine learning into the private equity investment process, providing access to alternative and proprietary data sources from portfolio companies. This position is critical for developing high-value tactical workflows that drive all aspects of the investment process. Your work will directly influence billions of dollars in investment decisions.

The TL;DR
• Role: Full Time
• Type: Hybrid
• Location: Hybrid, NYC
• Pay: $100–$200 hourly
• Team: Collaborative, fast-moving team working with experienced professionals in AI/ML.
• Mission: Own due diligence, sourcing, or portfolio support initiatives to influence investment decisions.
• Tech Stack: Python, advanced queries, filters, data visualization tools, machine learning, natural language processing, random forests, linear regression, predictive modeling.

What You’ll Actually Do
• Develop: Develop predictive models and proprietary analytics from large datasets.
• Create: Create valuable insights from datasets directly with stakeholders.
• Architect: Architect and manage analytical projects from ideation to launch.
• Determine: Use alternative data sets to algorithmically determine optimal investments for clients.
• Conduct: Conduct competitive analysis to determine the soundness of investments using all relevant data sources.

The Must-Haves
• Background: Bachelor’s degree or a concentration in a highly quantitative or relevant data science field (e.g., Computer Science, Statistics, Mathematics, Finance), or demonstrate equivalent professional experience.
• Experience: Experience applying machine learning or adjacent fields, including natural language processing, random forests, linear regression, and predictive modeling. Proven ability to handle terabyte-sized datasets, uncover hidden patterns, and work collaboratively with managers to develop models.
• Skills: Proficiency in writing code in Python. Expertise in advanced querying, filtering, and data visualization tools for large datasets.
• Bonus: A strong interest in the venture capital and private equity space, coupled with a drive to make analytical and data-driven decisions.