- Location
- United States
- Workplace
- Remote
- Type
- Contract
- Department
- Professional / Experienced
- Source
- JOIN
Description
**Lightly AG** is an ETH and HSG spin-off pioneering in machine learning and computer vision. Backed by Y Combinator and top-tier investors, our technology is used by global leaders in autonomous driving, medical imaging, and visual inspection.
With a small but sharp team in Zurich, we’re looking for analytical and driven **Finance Professionals** to help us **translate complex finance concepts into AI-readable formats.**
This role suits someone with a background in finance, economics, or engineering with **experience in any of the following:**
- **Corporate Finance**
- **Private Equity / Venture Capital**
- **M&A / Investment Banking**
- **Asset Management**
- **Accounting**
The minimum expected time commitment for this position is **10 hours/week.**
## Tasks
You’ll be a good fit if you have **2-3 years of experience** working at top firms in investment banking and experience in **at least some of the following** tools:
- **Excel,** **PowerPoint**, and **Bloomberg** will be second nature as you help turn market intelligence into executed deals.
- **BAMSEC**
- **Edgar SEC**
- **FactSet**
- **Thomson Reuters Checkpoint**
- **BNA Tax Portfolios**
- **AlphaSense**
- **Accounting Software**
- **PitchBook**
- **Capital IQ**
- **Intralinks**
- **DealRoom**
## Requirements
You’re a strong candidate if you:
- **Are fluent in English**
- **Have strong analytical and quantitative skills** and are confident working with financial models and valuation frameworks
- **Communicate clearly and precisely**
- **Are based in Zurich**
## Benefits
- A front-row seat to how a YC-backed deep tech company is built
- Direct exposure to the inner workings of startup leadership
- Flexible working hours and the ability to shape your role over time
- Competitive compensation tailored to experience and scope of work
Send us your **CV and a short note** about why this role excites you. If you’ve handled similar tasks before, we’d love to hear specific examples.
We look forward to hearing from you!
