Senior Data Scientist

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Data Scientist based in Brazil.

Join a data-driven environment where advanced analytics, machine learning, and artificial intelligence are used to solve meaningful business challenges and drive measurable growth. As a Senior Data Scientist, you will design, deploy, and optimize predictive models that help consumer applications and web platforms improve user retention, conversion, personalization, and lifetime value. You will take ownership of the modeling lifecycle, from defining business problems and developing analytical approaches to deploying production-ready solutions and monitoring their performance. Working closely with data engineering, product, marketing, and business stakeholders, you will translate complex behavioral data into actionable insights and strategic recommendations. You will also leverage Large Language Models (LLMs) and AI-powered tools to develop intelligent analytics solutions and extract value from unstructured data. This fully remote opportunity offers a collaborative, international environment where technical excellence, independent thinking, and continuous innovation are highly valued.

Accountabilities

Develop, deploy, and maintain predictive models for lifetime value (LTV), churn, and retention across mobile applications and web-based products.
Own the modeling roadmap for your area of responsibility, managing the complete lifecycle from problem definition and feature development to production deployment and ongoing performance monitoring.
Build cohort analyses and LTV models that inform user acquisition budgets, customer acquisition strategies, and payback period decisions.
Develop user segmentation and propensity models to support personalization, audience targeting, marketing optimization, and improved customer engagement.
Continuously improve model accuracy and reliability through validation, backtesting, performance monitoring, and the integration of new data sources.
Design and conduct experiments, statistical analyses, and causal inference studies to measure the actual impact of product changes, marketing campaigns, and business initiatives.
Identify early behavioral signals and predictive indicators associated with conversion, churn, engagement, and long-term customer value.
Apply LLMs and machine learning techniques to classify unstructured text and behavioral signals into meaningful interest categories, product preferences, and brand intent.
Collaborate with data engineering teams to productionize models, establish reliable data pipelines, and maintain the quality and consistency of model inputs.
Build internal AI-powered analytics tools and data products that improve analytical efficiency, automate workflows, and support data-informed decision-making.
Communicate model results, assumptions, limitations, and confidence levels clearly to stakeholders and leadership, translating technical findings into practical business recommendations.
Establish modeling standards, technical documentation, validation practices, and reusable best practices to ensure consistency and maintainability across data science initiatives.
Mentor analysts and fellow data scientists, share technical knowledge, and contribute to the continuous improvement of the team's analytical capabilities.
Requirements
At least five years of professional experience in data science, statistical modeling, or machine learning, ideally working with consumer applications, web platforms, or behavioral datasets.
Strong Python programming skills, including experience with pandas, scikit-learn, or comparable data science and machine learning libraries.
Advanced SQL skills and the ability to extract, transform, analyze, and interpret complex datasets efficiently.
Demonstrated experience deploying machine learning models into production environments and monitoring their performance over time.
Strong foundations in statistics, predictive modeling, forecasting, experimentation, and quantitative analysis.
Practical experience with LLM APIs, prompt engineering, or the development of AI-powered analytical tools and applications.
Regular use of AI tools as part of the development workflow, such as Claude, Claude Code, Claude Cowork, or comparable AI-assisted tools.
Strong analytical judgment and the ability to work with incomplete, noisy, or ambiguous data, structuring problems carefully before selecting appropriate methods and technologies.
Excellent communication skills, including the ability to explain complex analytical findings, model assumptions, and technical trade-offs in clear business terms.
Fluency in English and availability for at least four hours of overlap with US Eastern business hours.
Strong problem-solving skills, attention to detail, intellectual curiosity, and a proactive approach to learning new methods and improving existing models.
Ability to work independently in a remote environment while collaborating effectively with distributed teams and business stakeholders.
Preferred qualifications:
Experience working with subscription-based or advertising-monetized consumer applications.
Familiarity with recommendation systems, personalization techniques, and audience segmentation for advertising or targeted engagement.
Experience with cloud-based machine learning platforms, particularly AWS SageMaker or Google Cloud Vertex AI.
Knowledge of MLOps practices and workflow orchestration tools such as MLflow, Airflow, or dbt.
Familiarity with Bayesian methods, uplift modeling, multi-armed bandits, or other advanced approaches to experimentation and optimization.
Experience developing scalable data products and translating predictive insights into measurable improvements in business performance.
Benefits
Competitive monthly compensation: USD $4,000–$5,500, depending on experience, seniority, and technical skills.
Fully remote work: Work remotely from Brazil while contributing to projects for an international client.
Full-time engagement: A full-time contractual position administered through a payroll platform.
International collaboration: Work with distributed teams and contribute to data science initiatives with business impact.
Advanced technology exposure: Apply machine learning, predictive analytics, LLMs, and AI-powered development tools to real-world challenges.
Professional development: Expand your expertise in production machine learning, experimentation, AI applications, and modern data science practices.
Technical ownership: Lead modeling initiatives and influence analytical strategy, model development standards, and production implementation.
Collaborative environment: Partner with data engineering and business stakeholders to develop solutions that support product growth and strategic decision-making.
Continuous learning: Work in a technically challenging environment that encourages experimentation, independent thinking, and the adoption of emerging AI technologies.