Data Scientist
Lightning AI
| Company | Lightning AI |
| Category | Data & Analytics |
| Location | New York |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 13 Aug 2026 |
| Last verified | 13 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Who We Are
Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.
Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.
We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.
The Way We Work
The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:
Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.
Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.
Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.
Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.
Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.
Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.
About the Role
We are looking for a Data Scientist to drive informed decision-making across the company as we become the first fully-integrated neocloud for AI training. This individual will work closely with Product, Engineering, and Sales leadership, answering key questions about product usage, proactively surfacing trends, and supporting our growth in a data-driven way.
You will join the Product Team and report to our VP of Product.
This is a hybrid role based in New York City, NY with in-office requirements of 2 days per week.
What You’ll Do
Partner with Product, Sales, and Engineering to define questions that matter and identify product features and behaviors that drive business outcomes
Analyze small business and enterprise usage patterns to sharpen Ideal Customer Profiles and guide Sales prospecting priorities
Quantify the health of our data center business and test the impact of systematic sales, service, and operations improvements
Work with engineering to design, build, and maintain data pipelines that support timely and accurate analyses
Write SQL (and occasional scripts) to explore data, automate recurring workflows, and answer ad-hoc questions
Design Looker and Data Studio dashboards and reports that make findings easy for stakeholders to act on
Monitor trends and surface opportunities or risks early, before they show up in routine reporting
Help grow a data-driven culture by sharing practices and enabling teams to use analytics tools on their own
What You’ll Need
5+ years in a Data Science or Product Analyst role
Proficient using SQL to query large databases
Strong skills using Python to write scripts and run statistical analysis
Hands-on experience with BI tools (Looker or similar) and cloud data infrastructure — AWS (e.g. S3, EFS) and/or GCP (e.g. BigQuery, Looker Studio)
A strong bias for action, and the ability to make clear decisions and recommendations in the presence of uncertainty
Experience on a growing data team at a startup, preferred
Experience designing and building foundational data models from scratch,