Data Scientist - RealAdvisor

• 3+ years in data science, analytics.
• Python and SQL skills for data manipulation, modeling, and production prototyping.
• Experience consolidating diverse data sources and building reliable analytical pipelines.
• Experiment design and causal inference methods.
• Practical ML experience (classification, regression, segmentation) and model evaluation.
• Product analytics tools experience.
• Ability to build dashboards and present insights clearly (Looker, Tableau, Power BI, or similar).
• Business mindset: translates technical results into measurable business outcomes.
• Proactive: takes initiative, owns end‑to‑end delivery, and can work with limited supervision.

• Experience with subscription businesses and monetization models.
• Familiarity with marketing attribution and LTV modeling.
• Frontend or backend dev experience to implement tracking or small UI changes.
• Knowledge of data engineering tools (Airflow, dbt, Spark).
• Experience working across international markets and multilingual datasets.

• Fully remote.
• Full-time freelance role.
• Competitive monthly rate.

We are looking for a business‑oriented Data Scientist to join RealAdvisor. You will consolidate and analyze diverse data sources (product, marketing, transactional), build ML models and experiments to drive subscription growth, and translate insights into concrete product and marketing actions. This is a hands‑on, full‑stack role: you will query databases, run experiments, build models, and present clear data stories to stakeholders.

• Consolidate and aggregate data from product, marketing, CRM, and transactional systems into reliable datasets.
• Perform product analytics to measure feature usage, user journeys, and retention drivers.
• Design and run experiments (A/B tests, cohort analysis, population and variation control) to validate hypotheses and measure causal impact.
• Build ML models for revenue optimization, churn prediction, segmentation, and subscription forecasting.
• Analyze marketing performance to measure acquisition efficiency, LTV, and campaign ROI.
• Translate analysis into action: propose product changes, growth experiments, and operational KPIs.
• Report results using concise dashboards, reports, and data storytelling for non‑technical stakeholders.
• Work cross‑functionally with product, marketing, engineering, and country managers to standardize metrics and training.
• Maintain data quality: identify polluted data, propose remediation, and implement robust ETL checks.