Senior Data Scientist (Full Stack)

remotehiro; Deutschland; Remote; EUR 60,000 - 90,000; Full time

Job summary

Buffer is seeking a primary Data Scientist to support its product and growth teams with insightful analysis. This role involves building and maintaining business models and leading complex analyses to uncover new opportunities.

The ideal candidate will have significant experience in a SaaS environment and be proficient in SQL and Python or R. Familiarity with modern analytics stacks and A/B testing is also essential for this position.

Qualifications

• Significant experience as a Data Scientist in a SaaS environment.
• Deep hands-on experience with SQL-based data warehouses.
• Fluency in SQL and advanced experience using Python or R for analysis.

Responsibilities

• Serve as Buffer's primary Data Scientist supporting product and growth teams.
• Build and maintain behavioral and business models.
• Lead complex analyses to identify growth opportunities.

Skills

Experience as a Data Scientist in SaaS

Proficiency in SQL

Expertise in Python or R

Strong statistical analysis skills

Understanding of SaaS metrics

Experience with BI tools like Metabase

Experience with A/B testing

Analytical model building

Tools

BigQuery

Metabase

dbt

Segment

Job description

What you'll do

• Serve as Buffer's primary Data Scientist, supporting product and growth teams with analysis that informs decisions and prioritization
• Build and maintain behavioural and business models across acquisition, activation, engagement, retention, and monetization
• Lead complex analyses and research to identify product and growth opportunities, not just validate existing ideas
• Design, analyze, and interpret experiments across product and growth initiatives, including A/B tests and incrementality studies
• Partner with cross-functional teams to define success metrics, evaluation frameworks, and clear decision criteria
• Develop reusable datasets, models, and reporting patterns that reduce ad-hoc requests and increase self-serve capability
• Help evolve Buffer's data systems, definitions, and measurement approach as we invest in analytics product features and personalization
• Use AI-assisted tools to streamline analysis, accelerate insight generation, and make data more accessible across the organization
• Communicate findings through clear narratives, documentation, and recommendations that influence decisions at multiple levels
• Improve the reliability of our analytics foundations through better modelling patterns, data quality checks, documentation, and clear sources of truth
• Partner with engineering to strengthen the data platform over time, including clearer ownership boundaries, better observability, and fewer fragile workflows
• Help shape our approach to AI-assisted analytics responsibly, including safer defaults, governance considerations, and a bias toward trusted semantic layers over free-form querying

Qualifications

• You care deeply about creators, small businesses, and individuals, and are motivated by helping them succeed through better product and data-informed decisions
• Significant experience as a Data Scientist (or equivalent) in a SaaS or product-led growth (PLG) environment, with a strong understanding of SaaS metrics, growth loops, and monetization dynamics
• Deep hands-on experience with modern analytics stacks, including SQL-based data warehouses (BigQuery preferred; Snowflake or Redshift also relevant), BI tools such as Metabase, Mixpanel, Looker, or Mode, data transformation frameworks like dbt, and event tracking platforms like Segment
• Strong foundation in statistical analysis and causal reasoning, with fluency in SQL and advanced experience using Python or R for analysis, modelling, and data visualization
• Proven track record of owning complex, ambiguous analytical problems end-to-end, from framing and data exploration through to recommendations that influence product and business decisions
• Experience designing, analyzing, and interpreting experiments, including A/B testing, incrementality, and causal analyses, with a strong sense of methodological tradeoffs
• Experience building and maintaining analytical models across the full customer lifecycle, including acquisition, activation, engagement, retention, and monetization
• Demonstrated ability to balance high-impact strategic work with day-to-day analytical support, while systematically reducing ad hoc requests through better tooling, documentation, and self-serve systems
• Strong cross-functional partner to product managers, marketers, engineers, and designers, able to influence direction through data rather than operating as a service function
• Skilled at translating complex, messy data and ambiguous questions into clear metrics, narratives, and actionable insights for a wide range of stakeholders
• High degree of ownership and judgment, comfortable acting as the primary or most senior Data Scientist within a small team and shaping how data work gets done
• Experience leveraging modern data and AI-assisted tools to increase analytical leverage, improve insight accessibility, and scale impact across the organization

Nice to have (strong plus)

• Experience in an analytics engineering style role (dbt-first modelling, metric layers, curated datasets, data quality testing)
• Familiarity with data orchestration and observability concepts (even if you have not owned them end-to-end)
• Experience working with privacy, governance, and access controls in analytical environments
• Practical experience using LLM tools for analysis in a way that is secure and auditable (for example: working only on approved datasets, avoiding sensitive fields, documenting assumptions)