About the company
Join a data-driven environment where advanced analytics, machine learning, and artificial intelligence are used to solve meaningful business challenges and drive measurable growth.
Responsibilities
- Develop, deploy, and maintain predictive models for lifetime value (LTV), churn, and retention across mobile applications and web-based products
- Own the modeling roadmap, managing the complete lifecycle from problem definition to production deployment and performance monitoring
- Build cohort analyses and LTV models that inform user acquisition budgets and payback period decisions
- Develop user segmentation and propensity models for personalization, audience targeting, and marketing optimization
- Continuously improve model accuracy through validation, backtesting, and integration of new data sources
- Design and conduct experiments, statistical analyses, and causal inference studies
- Apply LLMs and machine learning techniques to classify unstructured text and behavioral signals
- Collaborate with data engineering teams to productionize models and establish reliable data pipelines
- Build internal AI-powered analytics tools and data products
- Communicate model results and assumptions clearly to stakeholders
- Mentor analysts and fellow data scientists
Requirements
- At least 5 years of professional experience in data science, statistical modeling, or machine learning
- Strong Python programming skills, including pandas, scikit-learn, or comparable libraries
- Advanced SQL skills
- Demonstrated experience deploying machine learning models into production environments
- Strong foundations in statistics, predictive modeling, forecasting, experimentation, and quantitative analysis
- Practical experience with LLM APIs, prompt engineering, or AI-powered analytical tools
- Regular use of AI tools such as Claude, Claude Code, Claude Cowork, or comparable
- Excellent communication skills
- Fluency in English and availability for at least 4 hours of overlap with US Eastern business hours
- Preferred: experience with subscription-based or advertising-monetized consumer applications
- Preferred: familiarity with recommendation systems and personalization techniques
- Preferred: experience with AWS SageMaker or Google Cloud Vertex AI
- Preferred: knowledge of MLOps practices and tools such as MLflow, Airflow, or dbt
- Preferred: familiarity with Bayesian methods, uplift modeling, multi-armed bandits
Conditions
- Competitive monthly compensation: USD $4,000–$5,500
- Fully remote opportunity in a collaborative, international environment