Brillio - Principal Data Scientist
Requirements
• Experience Range: 15 - 18 years of experience in advanced data science roles, with extensive leadership in designing and deploying statistical and machine learning solutions • 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 • 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 • 1. Master’s or PhD degree in Data Science, Statistics, Computer Science, Mathematics, or a related field • 2. Relevant certifications in machine learning, statistical analysis, or advanced data science • Know more about DAE: https://www.brillio.com/services-data-analytics/ • Know what it’s like to work and grow at Brillio: https://www.brillio.com/join-us/ • Equal Employment Opportunity Declaration
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
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