🕒 24 days ago
Develop statistical and machine learning models, analyze complex datasets, and deploy scalable forecasting and predictive solutions while translating findings into actionable recommendations for stakeholders.
Data Scientist - R01572015
🕒 24 days ago
Statistical AnalysisHypothesis TestingRegression AnalysisTime-series Forecasting
📜 Description
- Design statistical models using hypothesis testing, regression, and forecasting to generate business insights.
- Develop classification, prediction, and probabilistic graph models with Python, PySpark, and R.
- Analyze data using SAS, SPSS, and R Studio to support data-driven decisions.
- Build, train, and deploy models with machine learning frameworks including TensorFlow, PyTorch, and scikit-learn.
- Apply time-series methods such as exponential smoothing, ARIMA, and ARIMAX to analyze trends and predict outcomes.
- Manage production model deployment and lifecycle processes, validate data quality, and present findings to stakeholders.
🛠️ Requirements
- Design and implement robust statistical models using advanced hypothesis testing, regression, and forecasting techniques to deliver actionable business insights
- Develop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models utilizing Python, PySpark, and R
- Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-making
- Build, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
- Apply advanced time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX, to analyze trends and predict outcomes
- Streamline model deployment and lifecycle management in production environments using KubeFlow and BentoML
- Implement and validate data quality checks with Great Expectations and Evidently AI to ensure dataset integrity
- Present complex data findings to stakeholders, translating insights into actionable recommendations that drive business outcomes
- Advanced application of hypothesis testing methodologies, including T-Test and Z-Test
- Expert-level regression analysis (linear and logistic) for predictive modeling
Full job description
Data Scientist
Job requirements
Experience Range: With at least 4 years of hands-on experience in advanced data science, including statistical analysis and machine learning, and up to 6 years in similar roles Key Responsibilities:
Required Skills:
Preferred Skills:
Desired Qualifications:
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