Lead Data Scientist

People In AI; New York (NY); Hybrid; USD 300,000 - 350,000; Full time

Job summary

People In AI is seeking a Lead Data Scientist for a global investment firm in New York City. The role blends pre-deal diligence with post-deal value creation, applying data, machine learning, and AI to influence investment decisions across portfolio companies.

You will build models in Python, work with Databricks and Snowflake, mentor teammates, and present insights to senior investment leaders. The position is hybrid, with high visibility and direct impact on outcomes.

Qualifications

• Strong data science and machine learning experience.
• Consulting-style problem solving and strong commercial judgement.
• Experience identifying business problems, not just implementing predefined solutions.
• Confidence working with senior stakeholders in fast-paced environments.
• Experience managing projects and mentoring junior team members.

Responsibilities

• Turn ambiguous business problems into clear analytical approaches.
• Build models, analyses, and PoCs in Python.
• Identify opportunities to improve commercial and operational performance.
• Partner with portfolio companies to ensure solutions are adopted in practice.
• Manage multiple workstreams and mentor junior team members.
• Communicate findings clearly to senior stakeholders.

Skills

Python

Machine learning

Databricks

Snowflake

Business problem solving

Stakeholder engagement

Mentoring

Leadership

Job description

Lead Data Scientist, Private Equity & Value Creation

Compensation: $300,000 - $350,000 TC

Location: New York City - Hybrid

A leading global investment firm using data and AI to drive better investment decisions and portfolio performance.

This Data Science team works across the full private equity lifecycle, from evaluating potential investments to helping portfolio companies deliver measurable value through data, machine learning, and AI.

The Role

Your time will be split roughly 50/50 between pre-deal diligence and post-deal value creation.

Pre-deal, you'll work with investment teams to generate fast, decision-grade insights that can influence whether a deal moves forward. Post-deal, you'll partner with portfolio companies on practical initiatives such as churn prediction, marketing efficiency, pricing, inventory, and sales performance.

This is a hands-on, commercially focused role with direct exposure to investment teams and senior business leaders.

What You'll Do

• Turn ambiguous business problems into clear analytical approaches.
• Build models, analyses, and PoCs in Python.
• Identify opportunities to improve commercial and operational performance.
• Partner with portfolio companies to ensure solutions are adopted in practice.
• Manage multiple workstreams and mentor junior team members.
• Communicate findings clearly to senior stakeholders.

What You'll Bring

• Strong data science and machine learning experience.
• Consulting-style problem solving and strong commercial judgement.
• Experience identifying business problems, not just implementing predefined solutions.
• Confidence working with senior stakeholders in fast-paced environments.
• Experience managing projects and mentoring junior team members.
• Python
• Databricks / Snowflake
• Financial, consumer, web traffic, location, workforce, sentiment, and other alternative datasets

Why Join?

You’ll gain rare exposure to how top-tier investment decisions are made while working across a wide range of industries and business problems.

The role offers high visibility, significant ownership, and the opportunity to see your work directly influence both investment decisions and portfolio company performance.

The team is pragmatic and impact-focused: the goal is to solve the commercial problem, whether that means building a model, creating a quick analytical solution, or using an existing platform.

About People In AI

People In AI is a specialist recruitment business connecting exceptional talent with ambitious companies building and applying artificial intelligence, machine learning, and data technology.