Senior Data Science Lead - R01571251

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