Senior Data Science Lead - R01571251

Job description

Senior Data Science Lead

Job requirements Experience Range: With at least 8 years of experience in data science and advanced analytics, including recent leadership roles spanning up to 12 years Key Responsibilities: - Lead the design, development, and implementation of advanced statistical models and machine learning solutions to address complex business challenges and deliver measurable impact - Drive end-to-end data science project lifecycles, overseeing data exploration, hypothesis testing, feature engineering, model selection, and validation - Apply regression, classification, and forecasting techniques such as ARIMA, ARIMAX, exponential smoothing, and decision trees to generate predictive analytics and actionable insights - Collaborate with cross-functional teams to translate business objectives into actionable data science strategies and ensure alignment with organizational goals - Build, evaluate, and deploy scalable machine learning models using Python, PySpark, R, TensorFlow, PyTorch, and Sci-Kit Learn - Monitor data quality, bias detection, and model performance using Great Expectations and Evidently AI, ensuring robust analytics outcomes - Mentor and guide junior data scientists, providing technical leadership, conducting code reviews, and promoting best practices in statistical analysis and machine learning - Present findings and insights to stakeholders through clear visualizations and presentations, facilitating data-driven decision making Required Skills: - Advanced proficiency in Python and PySpark for data processing and modeling - Expertise in statistical analysis, including hypothesis testing, t-tests, and z-tests - Strong knowledge of regression techniques (linear, logistic) and classification algorithms (decision trees, SVM) - Hands-on experience with probabilistic graphical models for complex data relationships - Proficiency with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet - Experience with forecasting methods including exponential smoothing, ARIMA, and ARIMAX - Competence in data quality and monitoring tools such as Great Expectations and Evidently AI - Working knowledge of SAS or SPSS for statistical analysis and computing - Familiarity with R and R Studio for advanced analytics - Understanding of distance metrics such as Hamming, Euclidean, and Manhattan Preferred Skills: - Experience deploying machine learning models in production environments using KubeFlow or BentoML - Expertise in developing scalable data pipelines for machine learning workflows - Knowledge of advanced feature engineering and dimensionality reduction techniques such as PCA and t-SNE - Background in model interpretability and explainable AI methodologies (e.g., SHAP, LIME) - Exposure to real-time analytics and streaming data platforms such as Apache Kafka or Spark Streaming Desired Qualifications: - Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline - Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate (preferred) - Formal training or certification in advanced statistical analysis or machine learning frameworks