Growth Data Scientist
New
N
NivodaJewelry Marketplace
EuropeFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLSalesforceSnowflakeHubSpotdbt
Requirements
- 4+ years in data science, applied statistics, growth analytics, machine learning, or similar role.
- Strong Python.
- Strong SQL.
- Experience building practical models that influence business decisions.
- Good understanding of experimentation.
- Good understanding of statistical reasoning.
- Good understanding of measurement.
- Ability to work with messy, incomplete, or fast-changing data.
- Strong commercial judgement.
- Clear communication with technical and non-technical stakeholders.
- Bias for action, with a high quality bar.
- Comfortable operating autonomously in a scale-up environment.
- Highly Desirable: Marketplace, B2B, SMB, logistics, fintech, e-commerce, or high-growth scale-up experience.
- Highly Desirable: Lead scoring, propensity modelling, churn prediction, activation modelling, segmentation, or LTV modelling.
- Highly Desirable: Working with CRM, marketing, product, or commercial data.
- Highly Desirable: Snowflake, dbt, HubSpot, Salesforce, or modern data/ML workflows.
- Highly Desirable: Partnering with Growth, Sales, Marketing, Product, or Revenue Operations teams.
Responsibilities
- Build and improve scoring models to identify high-potential leads, customers, and commercial opportunities (lead scoring, activation prediction, churn risk, expansion potential, customer segmentation, opportunity prioritisation).
- Help Growth, Sales, and Marketing focus their effort where it is most likely to create value.
- Analyze customer, marketplace, CRM, marketing, and commercial data to identify patterns explaining wins, underperformance, and next steps.
- Turn messy data into clear recommendations, not just dashboards.
- Help Growth and Product teams design better experiments, define success metrics, interpret results, and understand uncertainty.
- Work with imperfect real-world data, including low sample sizes, noisy attribution, weak instrumentation, and unclear funnel definitions.
- Write strong SQL and Python, work with data in Snowflake, create reproducible datasets, and build lightweight workflows.
- Drive action by influencing decisions such as sales prioritisation, lifecycle targeting, customer segmentation, and growth strategy.
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