Data Scientist - R01571773

Experience Range: With at least 4 years of experience in advanced data science, statistical analysis, and machine learning model development, including hands-on work with large datasets and production model deployment. Key Responsibilities: - Design, develop, and deploy advanced statistical and machine learning models using Python, R, and specialized frameworks to address complex business challenges - Conduct rigorous statistical analysis, including hypothesis testing, regression analysis, and probabilistic modeling, to extract actionable insights from large-scale data

Required Skills: - Advanced proficiency in Python and PySpark for data analysis and model development - Expertise in statistical analysis and computing using SAS or SPSS

- Familiarity with distance metrics such as Hamming, Euclidean, and Manhattan Distance - Working knowledge of R and R Studio for statistical modeling - Experience with data validation and monitoring tools such as Great Expectations and Evidently AI

- Advanced proficiency in Python and PySpark for data analysis and model development - Expertise in statistical analysis and computing using SAS or SPSS - Hands-on experience with regression techniques including linear and logistic regression

- Proficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTK - Background in cloud-based analytics platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform) - Exposure to automated machine learning (AutoML) workflows

- 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 provider (e.g., Microsoft Certified: Azure Data Scientist Associate, IBM Data Science Professional Certificate) Employment type

- Develop, optimize, and maintain forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to support business planning - Build, train, and evaluate classification and regression models using ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras, MXNet, CNTK) - Deploy and monitor models in production environments using scalable cloud-native tools such as KubeFlow and BentoML

- Experience deploying machine learning models using KubeFlow or BentoML - Proficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTK - Background in cloud-based analytics platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform)

- Conduct rigorous statistical analysis, including hypothesis testing, regression analysis, and probabilistic modeling, to extract actionable insights from large-scale data - Implement and validate data quality checks using tools such as Great Expectations and Evidently AI to ensure data and model integrity - Collaborate with cross-functional teams to define data-driven strategies, translate business requirements into analytical solutions, and present findings to stakeholders

- Working knowledge of R and R Studio for statistical modeling - Experience with data validation and monitoring tools such as Great Expectations and Evidently AI Preferred Skills: