Product Data Scientist SaaS at Jobgether
At a glance
- Company
- Jobgether
- Location
- Germany, DE
- Work arrangement
- Remote
- Employment type
- Full-time
- Experience
- 2–5 years
- Working hours
- 40 hours
- Visa sponsorship
- Offered, according to the listing
- Posted
✨ The Stack take on this Jobgether job
- 🏢 Jobgether has 93 jobs here, 93 with visa sponsorship see all
- 📊 16 Data Scientist jobs in Germany in the last 60 days see them
- 🛂 Visa: EU Blue Card. Minimum salary: €50,700 a year guide
- 💡 No salary shown. Ask Jobgether if it is above the minimum.
Updated 2026-11-02, from our data and official sources.
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About the role
Responsibilities:
- Own and improve the experimentation practice: define hypothesis processes, metric selection, power calculations, test duration and decision rules.
- Design, review and validate A/B and multi-variant experiments alongside Product and business teams prior to launch.
- Analyze experiment results, interpret statistical findings, communicate limitations, and recommend actionable next steps.
- Map and analyze the full customer journey (acquisition, conversion, usage, payments, retention) to identify growth and monetization opportunities.
- Build cohort, funnel and LTV/CAC analyses to guide marketing investment and product prioritization.
- Collaborate with Payments, Marketing and Data Engineering to measure payment performance, optimize advertising signals, and integrate value signals into platforms.
- Evangelize data-informed decisions and make analytical tools/processes accessible to product teams.
Requirements:
- 3–5 years in product or growth data science, ideally in SaaS or high-traffic consumer products.
- Strong hands-on experience with experimentation and statistics (A/B testing, power analysis, significance, bias checks).
- Advanced SQL and Python skills; experience with product analytics platforms and data warehouses.
- Practical experience with cohort analysis, funnel analysis, LTV/CAC modeling and payment/advertising datasets.
- Clear communicator who translates analysis into concise business recommendations and can work cross-functionally in a remote, international environment.
Responsibilities
Lead data-informed decision-making across international SaaS products, design and validate experiments, and optimize product and marketing initiatives. Collaborate with Data, Product, Marketing, Payments, and executive teams to translate insights into measurable improvements across the customer journey.
Requirements
3-5 years of experience in Product Data Science or Growth Data Science, preferably in SaaS or e-commerce with high-traffic products. Hands-on experimentation with statistical analyses, advanced SQL and Python, and strong product analytics knowledge; experience with data warehouses and payment/advertising analytics is a plus.
Key skills
- SQL — in 521 other listings on The Stack
- Python — in 1159 other listings on The Stack
- experimental design — in 21 other listings on The Stack
- A/B testing — in 122 other listings on The Stack
- statistics — in 49 other listings on The Stack
- cohort analysis — in 14 other listings on The Stack
- LTV modeling — in 2 other listings on The Stack
- data warehouses — in 2 other listings on The Stack
- product analytics platforms
- payment data analysis
- advertising analytics
- data storytelling
On The Stack (as of 2026-11-02)
Frequently asked questions about Jobgether
Is this Product Data Scientist SaaS role still open?
It was posted on 2026-11-02 and is listed as active on The Stack. Confirm on Jobgether's application page before applying.
Does Jobgether offer visa sponsorship?
This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Jobgether's site.
Where is this role located?
The listing gives the location as Germany, DE. It is marked remote.
What skills does this role ask for?
The listing names: SQL, Python, experimental design, A/B testing, statistics, cohort analysis, LTV modeling, data warehouses, product analytics platforms, payment data analysis, advertising analytics, data storytelling.