Data Scientist: Product & Analytics
New
GermanyFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- Several years of experience
- Required Skills
- PythonSQLData modelingdbt
Requirements
- Several years of experience working as a Data Scientist, ideally within a modern product-focused or technology-driven environment.
- Strong programming skills in Python and SQL, with experience applying them to real-world data challenges.
- Proven experience building production data products or analytical systems that have delivered measurable business impact.
- Strong understanding of experimentation methodologies, statistics, hypothesis testing, and data-driven decision-making.
- Experience working with modern data warehouses, transformation tools, and analytics platforms.
- Knowledge of dbt is a plus.
- Experience collaborating directly with Product, Growth, Marketing, or other business teams to solve strategic problems.
- Ability to investigate complex datasets, identify meaningful patterns, and communicate insights effectively.
- Strong ownership mindset with the ability to work independently, navigate ambiguity, and prioritize impactful solutions.
- Experience in B2C SaaS, subscription products, or product-led growth environments is an advantage.
Responsibilities
- Build, maintain, and improve production data products and analytical systems that support key business and product decisions.
- Design and analyze experiments in collaboration with Product and Growth teams, defining success metrics, evaluating outcomes, and providing actionable recommendations.
- Develop reliable analytical foundations through data modeling, pipeline improvements, data quality monitoring, and scalable analytics solutions.
- Partner with stakeholders to understand complex business challenges, explore data, identify opportunities, and recommend effective solutions.
- Translate complex analyses into clear insights for both technical and non-technical audiences, enabling better prioritization and decision-making.
- Continuously improve analytics infrastructure, semantic data models, experimentation frameworks, and future data-driven capabilities.
- Collaborate with Data Engineering teams to strengthen the overall data platform and ensure trusted, accessible information across the organization.
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