Senior Data Science Lead - R01571450

Job requirements

Experience Range: With at least 8 years of experience in data science, statistical modeling, and advanced analytics, including up to 12 years leading advanced data science initiatives Key Responsibilities:
• Lead the design and implementation of advanced statistical models and machine learning algorithms to address complex business challenges and deliver actionable insights
• Develop, validate, and optimize predictive and forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to improve business forecasting accuracy
• Conduct rigorous hypothesis testing, including T-Tests and Z-Tests, to inform experimental design and support data-driven decision making
• Collaborate with cross-functional teams to define project requirements, ensure alignment with organizational objectives, and deliver impactful data science solutions
• Oversee data preprocessing, feature engineering, and data quality assessments utilizing tools such as Great Expectations and Evidently AI
• Mentor and guide team members in the use of Python, PySpark, R, and machine learning frameworks including TensorFlow, PyTorch, and Sci-Kit Learn
• Implement and manage end-to-end data science workflows and model deployment pipelines using KubeFlow and BentoML
• Evaluate and interpret model results, ensuring statistical rigor and effectively communicating findings to stakeholders
Required Skills:
• Python and PySpark for data analysis and model development
• Statistical analysis and computing using SAS or SPSS
• Hypothesis testing methodologies including T-Test and Z-Test
• Regression techniques such as linear and logistic regression
• Development and deployment of machine learning models using TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
• Probabilistic graph models and classification algorithms including decision trees and SVM
• Time series forecasting methods including exponential smoothing, ARIMA, and ARIMAX
• Distance metrics such as Hamming, Euclidean, and Manhattan distances
• R and R Studio for statistical computing and visualization
• Data validation and monitoring tools including Great Expectations and Evidently AI
Preferred Skills:
• Advanced model interpretability and explainability techniques
• Experience with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google AI Platform
• Expertise in MLOps best practices for scalable model deployment
• Design and implementation of deep learning architectures for structured and unstructured data
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 Certified Data Scientist or TensorFlow Developer Certificate)
• Relevant certification in statistical analysis tools or platforms (such as SAS Certified Advanced Analytics Professional or Microsoft Certified: Azure Data Scientist Associate)