Senior Data Scientist
New
J
JobgetherConsumer applications
Based in Brazil; Work remotely from Brazil, At least four hours of overlap with US Eastern business hoursFull-TimeSenior
Salary4,000 - 5,500 USD per month
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Job Details
- Languages
- Fluency in English
- Experience
- At least five years of professional experience in data science, statistical modeling, or machine learning
- Required Skills
- PythonSQLMachine LearningPandasscikit-learnPrompt Engineering
Requirements
- Have at least five years of professional experience in data science, statistical modeling, or machine learning.
- Bring strong Python programming skills, including pandas, scikit-learn, or comparable libraries.
- Have advanced SQL skills for extracting, transforming, analyzing, and interpreting complex datasets.
- Have experience deploying machine learning models to production and monitoring their performance.
- Have strong foundations in statistics, predictive modeling, forecasting, experimentation, and quantitative analysis.
- Have practical experience with LLM APIs, prompt engineering, or AI-powered analytical tools and applications.
- Regularly use AI tools in the development workflow, such as Claude, Claude Code, Claude Cowork, or comparable tools.
- Be fluent in English and available for at least four hours of overlap with US Eastern business hours.
- Work independently in a remote environment and collaborate effectively with distributed teams and business stakeholders.
- Preferred: experience with subscription-based or advertising-monetized consumer applications.
- Preferred: familiarity with recommendation systems, personalization, and audience segmentation.
- Preferred: experience with AWS SageMaker or Google Cloud Vertex AI, and familiarity with MLOps tools such as MLflow, Airflow, or dbt.
Responsibilities
- Develop, deploy, and maintain predictive models for LTV, churn, and retention across mobile applications and web products.
- Own the modeling lifecycle from problem definition and feature development through production deployment and performance monitoring.
- Build cohort analyses and LTV models to inform user acquisition budgets, customer acquisition strategies, and payback decisions.
- Develop segmentation and propensity models for personalization, audience targeting, and marketing optimization.
- Design experiments, statistical analyses, and causal inference studies to measure product, campaign, and business impacts.
- Apply LLMs and machine learning to classify unstructured text and behavioral signals.
- Collaborate with data engineering to productionize models and establish reliable data pipelines.
- Build AI-powered analytics tools and data products that improve analytical efficiency and automate workflows.
- Communicate model results, assumptions, limitations, and confidence levels to stakeholders and leadership.
- Establish modeling standards and documentation, and mentor analysts and data scientists.
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