Original listing text, shown exactly as published by the company.
In this role, you are contributing by…
- Owning the sales data layer in the data warehouse:
- Designing, maintaining, and evolving the sales schemas in our data warehouse
- Ensuring data quality, consistency, and accessibility for the sales organisation
- Working closely with Data Engineering to align on data models and pipelines
- Independently leading data analytics and modelling with a focus on New Business MRR:
- Data queries and solid understanding of data management (SQL, BI tools)
- Business and financial modelling (forecasting, budget simulations, business analysis)
- Machine learning application (prediction models, clustering, decision trees, XGBoost)
- Use of AI tools (ChatGPT, Claude, Copilot or similar) to accelerate analysis and automate repetitive tasks
- Building internal tools and automations that remove manual effort from the team:
- Internal tooling development to support sales workflows and reporting
- Workflow automation using tools like Make, Zapier, Cowork or similar
- Desirable — proven experience in designing and implementing advanced analytics (data preparation, feature engineering, model validation and productisation)
- Co-designing, running, and scaling data-driven experiments from one market into global projects
How do we define success in this position?
- Sales schemas in the data warehouse are well-structured, reliable, and trusted by the organisation
- Insights delivered on time and under pressure — even when priorities shift
- Analytical models and internal tools adopted by the team that reduce manual effort and improve decision-making
- Measurable impact on New Business MRR through data-driven insights
Qualifications
What will help you thrive?
- You have 3+ years of professional experience
- You're fluent in English (any other language — Spanish, Portuguese, Polish — is a plus)
- Ideally a Data Science, Statistics, Math/Physics, Computer Science or quantitatively oriented background
- Strong technical expertise — SQL is a must, Python is highly valued:
- Solid experience designing and querying databases; ideally experience owning or contributing to data warehouse schemas
- Knowledge of Tableau / Power BI or other BI tools — ability to edit and create dashboards
- Python modelling experience; knowledge of key data science algorithms: clustering, regression, decision trees, XGBoost
- Experience with AI tools (ChatGPT, Claude, Copilot or similar) and workflow automation platforms (Make, Zapier, Cowork or similar)
- Comfortable working in a fast-paced, flexible environment without a fixed ticket queue — you set your own priorities based on business impact
- Ability to work under pressure and deliver rigorous insights to tight deadlines
- Experience in internal tooling development is a strong plus
- Prior experience in SaaS or marketplace business models is a plus
- Ability to package insights and communicate with stakeholders, including leading working sessions
- Proven experience working with multicultural teams and navigating complex organisations
What to Expect from Our Hiring Process
We like to keep things transparent and efficient! Here’s what the process usually looks like (though it might vary slightly depending on the role):
1️⃣ Intro Chat – A first call with our Talent Partner to explore mutual fit around relevant skills, value alignment, and motivation.
3️⃣Hiring Manager Interview – A deeper conversation about your background, aspirations, and experience with the Hiring Manager and your potential manager in this role. Take this chance to ask anything on your mind—it’s just as much about making sure we’re the right fit for you, too.
4️⃣Business Case – An exercise designed to understand how you approach real-life problems. You’ll then walk us through your approach in a collaborative discussion with the hiring manager and the team to discuss your thoughts and findings.
5️⃣ Additional/ Final Interview (Optional) – A final conversation with another stakeholder from our Global Team.
6️⃣ References & Offer!
Why You’ll Love It Here
