Principal Data Scientist

- Experience Range: 15 - 18 years of experience in advanced data science roles, with extensive leadership in designing and deploying statistical and machine learning solutions Key Responsibilities: 1. Design and implement robust statistical models and machine learning algorithms for large-scale data analysis and predictive analytics 2. Lead end-to-end development of data science projects, including hypothesis testing, regression analysis, classification, and forecasting 3. Collaborate with cross-functional teams to define business requirements, translate them into analytical solutions, and drive measurable impact 4. Optimize and automate data pipelines using Python, PySpark, and R, ensuring efficient data processing and feature engineering 5. Develop, validate, and maintain probabilistic graph models and advanced statistical computing frameworks 6. Utilize industry-leading ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn to build, train, and deploy models 7. Establish rigorous model evaluation and monitoring processes using tools like Great Expectations and Evidently AI 8. Mentor and guide junior data scientists, fostering technical excellence and continuous learning within the team Required Skills: 1. Expertise in hypothesis testing, including T-Test and Z-Test methodologies 2. Advanced proficiency in regression techniques (linear and logistic) 3. Strong programming skills in Python, PySpark, and R/R Studio 4. Hands-on experience with SAS and SPSS for statistical analysis and computing 5. In-depth knowledge of probabilistic graph models 6. Experience with forecasting methods such as Exponential Smoothing, ARIMA, and ARIMAX 7. Practical use of classification algorithms including Decision Trees and Support Vector Machines (SVM) 8. Proficiency with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet 9. Familiarity with distance metrics (Hamming, Euclidean, Manhattan) 10. Working knowledge of Kubeflow and BentoML for model deployment and orchestration

Key Responsibilities: 1. Design and implement robust statistical models and machine learning algorithms for large-scale data analysis and predictive analytics 2. Lead end-to-end development of data science projects, including hypothesis testing, regression analysis, classification, and forecasting 3. Collaborate with cross-functional teams to define business requirements, translate them into analytical solutions, and drive measurable impact 4. Optimize and automate data pipelines using Python, PySpark, and R, ensuring efficient data processing and feature engineering 5. Develop, validate, and maintain probabilistic graph models and advanced statistical computing frameworks 6. Utilize industry-leading ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn to build, train, and deploy models 7. Establish rigorous model evaluation and monitoring processes using tools like Great Expectations and Evidently AI 8. Mentor and guide junior data scientists, fostering technical excellence and continuous learning within the team Required Skills: 1. Expertise in hypothesis testing, including T-Test and Z-Test methodologies 2. Advanced proficiency in regression techniques (linear and logistic) 3. Strong programming skills in Python, PySpark, and R/R Studio 4. Hands-on experience with SAS and SPSS for statistical analysis and computing 5. In-depth knowledge of probabilistic graph models 6. Experience with forecasting methods such as Exponential Smoothing, ARIMA, and ARIMAX 7. Practical use of classification algorithms including Decision Trees and Support Vector Machines (SVM) 8. Proficiency with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet 9. Familiarity with distance metrics (Hamming, Euclidean, Manhattan) 10. Working knowledge of Kubeflow and BentoML for model deployment and orchestration Preferred Skills: 1. Experience implementing advanced model monitoring with Evidently AI 2. Expertise in data pipeline automation and orchestration using Kubeflow 3. Knowledge of emerging ML frameworks and architectures 4. Experience with large-scale distributed computing environments 5. Strong background in statistical validation and reproducibility best practices