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Data Scientist
Lojas Renner S.A.Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and validating forecasting models, utilizing statistical methods and machine learning algorithms. Proficient in Python and SQL for data manipulation, with a strong ability to translate business challenges into data-driven solutions.
Highest-signal resume keywords
Data Science ExperienceMachine Learning ProficiencyStatistical Modeling SkillsForecasting Model DevelopmentPython and SQL Proficiency
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Develop, validate, and enhance forecasting models across different forecast horizons and levels of granularity
- Build and evolve pipelines for data preparation, model training, evaluation, and inference
- Analyze trends, seasonality, events, and external factors that influence the data and forecast accuracy
- Conduct analyses and experiments to compare models and identify the best approaches, considering technical metrics and business impact
- Monitor model performance in production, identifying deviations, biases, and opportunities for improvement
- Investigate inconsistencies and propose improvements to data quality, feature engineering, and modeling processes
- Partner with Engineering and Technology teams to ensure the deployment, integration, and scalability of solutions
- Translate business challenges into analytical problems, presenting results and recommendations clearly and objectively
- Document experiments, technical decisions, and evaluation criteria, contributing to the continuous improvement of solutions
Requirements
What you’ll need- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Economics, Engineering, or a related field
- Practical experience in Data Science, Machine Learning, or statistical modeling
- Proficiency in Python and SQL for data manipulation, analysis, and modeling
- Experience developing time series, regression, and forecasting models
- Knowledge of statistical methods and Machine Learning algorithms applied to forecasting
- Experience with data preparation, feature engineering, and temporal validation
- Familiarity with forecast evaluation metrics such as MAE, RMSE, WAPE, MAPE, and Bias
- Experience with code versioning and experiment and model tracking
- Ability to develop organized, testable solutions that are ready for production environments
- Ability to understand business challenges and transform them into data-driven solutions
- Experience with hierarchical forecasting, forecast reconciliation, and different levels of granularity is a plus
- Knowledge of ARIMA, ETS, Prophet, tree-based algorithms, and neural networks applied to time series is a plus
- Experience with external variables, calendar events, and probabilistic forecasting is a plus
- Familiarity with Databricks, Apache Spark, and cloud platforms is a plus
- Knowledge of MLflow, Docker, CI/CD, and MLOps practices is a plus
- Experience with optimization, simulation, or demand planning is a plus
- Experience with automated monitoring, degradation detection, and model retraining is a plus
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Forecasting ModelsStatistical MethodsMachine Learning AlgorithmsTime Series AnalysisFeature EngineeringData PreparationModel Evaluation MetricsHierarchical ForecastingOptimization TechniquesAutomated Monitoring
Soft Skills
Analytical Problem SolvingClear CommunicationCollaboration with Engineering Teams
Tools & Technologies
DatabricksApache SparkMLflowDockerCI/CDCloud Platforms
Industry Keywords
Forecast AccuracyModel Performance MonitoringData Quality ImprovementTemporal ValidationDemand Planning