From Berlin to Boston: How Lisa Landed a $160k Remote Data Science Role
Lisa Weber spent eight months applying to US companies from Berlin. But she got nowhere - more than a hundred applications were all ghosted.
Then in three weeks, she landed a $160,000 remote role at a San Francisco fintech startup. More than double her Berlin salary.
Here's what actually worked.
The Problem
Lisa had the right credentials for the roles she was applying to:
- 7 years building ML fraud detection systems
- PhD in Machine Learning from TU Berlin
- Production experience with Python, PyTorch, AWS
- Portfolio of shipped projects
But she still rarely heard back on her applications.And the oens that did get back ghosted after an initial screening call.
Eight months of applying:
- 120+ applications
- 8% response rate
- 4 interviews that went nowhere
- Zero feedback
"I have a PhD and years of experience, but US companies treated me like I didn't exist. The few interviews I got ended in ghosting." — Lisa Weber
The problem wasn't her skills. It was the process. Cold applications from EU candidates don't make it past the filters that most US companies have in place.
What Changed
Lisa's colleague mentioned us after landing his own remote role. She was skeptical but submitted her profile anyway.
Week 1: Finding Her Angle
We looked at her background and saw a specific project we knew was bring hired for: real-time fraud detection ML for fintech.
Not "I do machine learning." More like "I built production fraud detection that processes 50,000 transactions per second."
That specificity matters. A lot.
Week 2: Direct Intros
We connected her with two Series B fintech startups in San Francisco. Both building ML fraud detection. Both needed exactly what she'd built before.
No job boards. No ATS systems. We got her direct introductions to hiring managers.
Week 3: Interviews
This led to five interviews across two companies in one week. Both made offers.
| Before | After |
|---|---|
| 120+ blind applications | 2 targeted companies |
| 8% response rate | 100% |
| Ghosted every time | Feedback within 24 hours |
| 8 months | 3 weeks |
The Logistics
Most guides skip this part. But figuring out contracts and taxes is where people get stuck. Lisa works as an independent contractor in Germany. The US company pays her through Deel.
Why it works:
- No US visa needed
- German labor law protects her
- Deel handles payroll and compliance
- German health insurance
- Clear tax structure
She considered setting up a German GmbH but decided against it for now. Too much overhead for a first US role.
Her agreement specifies:
- $160,000/year ($13,333/month)
- 40 hours/week
- 2-3 hours daily overlap with SF team (5-7pm Berlin)
- $3,000 home office budget
- 25 vacation days (she negotiated this—US standard is 10-15)
- 30 days notice either way
She has no equity yet as that will make onboarding difficult. However she has the option to re-negotiate that at six months.
Daily Schedule
- 9am-2pm: Deep work
- 2pm-5pm: Overlap time (meetings, code review)
- 5pm-7pm: Wrap up
- Evenings: Off
The team records meetings she misses and documents decisions. The async setup actually works.
Interview Prep
Lisa prepared for three weeks before interviews.
Technical:
- Two LeetCode problems daily (arrays, hashmaps, trees)
- System design practice from "Designing Data-Intensive Applications"
- ML fundamentals review (bias-variance, regularization)
- GitHub repo with clean code samples
Behavioral:
- Wrote 8 STAR stories (Situation, Task, Action, Result)
- Covered different scenarios (debugging, conflict, leadership)
- Practiced adapting them to different questions
Her questions:
- "What does your ML infrastructure look like?"
- "How do you handle model drift?"
- "What's the process for promoting models to production?"
What helped her was focusing more on showcasing her knowledge of system architecture and production systems, instead of simply grinding on Leetcode.
Six Months Later
Money
Berlin: €75,000, saving €500/month
Now: $160,000, saving €4,000/month
She paid off €15,000 in student loans in four months. She's now saving for a house.
"I am doing the same work I was doing before. Just for a US company. Now I can build savings instead of surviving month-to-month." — Lisa
Work
She's building ML systems that process 50,000+ transactions per second that has the potential to save the company $2M+ monthly. And she gets to work with ex-Google and ex-Stripe engineers.
The async culture is better than her old office job. US teams ship faster. She worked from Spain last month.
It's not all perfect though — the timezone means meeting-heavy afternoons, and she had to adjust to more direct communication. She also joined a coworking space where she goes twice a week for social interaction.
What Lisa Learned
On Finding Roles
"I wasted eight months applying to everything. When I got specific—production ML for fintech—it clicked."
"Cold applications don't work from Europe. You need warm introductions."
"Target remote-first companies, not ones that added remote during COVID."
On Interviews
"European interviews test coding. US interviews want system design and clear thinking. Practice explaining architecture out loud."
"Write out your STAR stories beforehand. US behavioral interviews are structured."
"Ask about their infrastructure and processes. Show you understand production."
The Pattern
What made this work:
- Specific positioning
- Direct introductions
- Proper preparation
- Clear logistics
- Fast execution
US companies need senior EU talent. But the traditional process is broken for international candidates.
Get Started
If you're tired of applications disappearing, getting ghosted, or settling for local comp — there's a different way.
We connect EU/UK technical professionals with US companies. Direct hiring manager access. No endless applications.