Data Scientist

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:

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

Required Skills:

Advanced application of hypothesis testing methodologies, including T-Test and Z-Test

Expert-level regression analysis (linear and logistic) for predictive modeling

Proficient programming in Python and PySpark for data manipulation and model development

Extensive experience with statistical analysis using SAS and SPSS

Hands-on expertise in probabilistic graph models for complex data relationships

Mastery of time series forecasting techniques (exponential smoothing, ARIMA, ARIMAX)

Implementation of classification algorithms such as decision trees and support vector machines (SVM)

Deep familiarity with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet

Calculation and application of distance metrics (Hamming, Euclidean, Manhattan)

Skilled in R and R Studio for statistical analysis and visualization

Preferred Skills:

Practical experience with Great Expectations and Evidently AI for advanced data validation

Proficiency in cloud-based model deployment tools such as KubeFlow and BentoML

Background in large-scale data processing and distributed computing environments

Expertise in feature engineering and model interpretability techniques

Familiarity with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google Cloud AI Platform

Desired Qualifications:

Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline

Certification in Data Science or Machine Learning from a recognized institution, such as Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate

Data Scientist - R01572015

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