RPL International
Data Scientist
Full Time · In Office · Miami, Florida (USA)
Posted Jul 7, 2026
Work Options
Positions
Job Type
Position Group
- **no C2C**
- Data Scientist
- The Data Scientist will support the Analytics team by applying machine learning and advanced analytics to improve business performance, operational decision-making, and customer experience. This individual will translate complex business challenges into data-driven solutions while owning projects from end to end, including data exploration, preparation, modeling, validation, deployment, and ongoing optimization.
- Develop Predictive & Analytical Models
- Design, build, validate, and enhance machine learning and statistical models that solve business challenges, including demand forecasting, customer behavior analysis, operational optimization, and business performance improvement.
- Data Exploration, Preparation & Validation
- Perform data profiling, exploration, cleansing, transformation, feature engineering, and quality validation to ensure accurate, reliable datasets for analysis and model development.
- Translate Business Needs into Data Solutions
- Partner with business leaders and cross-functional teams to understand objectives, define analytical approaches, establish success metrics, and deliver actionable insights.
- Deploy & Maintain Machine Learning Models
- Support the deployment of predictive models into production environments, monitor model performance, and continuously refine models to improve accuracy, scalability, and long-term reliability.
- Experimentation & Performance Analysis
- Design, execute, and evaluate experiments (including A/B testing) to measure the effectiveness of business initiatives, operational improvements, customer programs, and strategic decisions.
- Forecasting & Operational Planning
- Develop forecasting models and scenario analyses that support resource planning, capacity management, demand prediction, and operational decision-making.
- Communicate Insights & Drive Business Value
- Present analytical findings and recommendations in a clear, concise manner to both technical and non-technical stakeholders, helping drive informed business decisions.
- Continuous Improvement
- Identify opportunities to improve data quality, analytical methodologies, machine learning capabilities, and reporting processes while contributing to the growth of the organization's data science practice.
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Data Scientist
RPL International
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