Note: The job is a remote job and is open to candidates in USA. Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies. They are seeking a Principal Data Scientist to lead the development of advanced data science projects, utilizing statistical models and machine learning algorithms to drive impactful analytical solutions.
Responsibilities
- Design and implement robust statistical models and machine learning algorithms for large-scale data analysis and predictive analytics
- Lead end-to-end development of data science projects, including hypothesis testing, regression analysis, classification, and forecasting
- Collaborate with cross-functional teams to define business requirements, translate them into analytical solutions, and drive measurable impact
- Optimize and automate data pipelines using Python, PySpark, and R, ensuring efficient data processing and feature engineering
- Develop, validate, and maintain probabilistic graph models and advanced statistical computing frameworks
- Utilize industry-leading ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn to build, train, and deploy models
- Establish rigorous model evaluation and monitoring processes using tools like Great Expectations and Evidently AI
- Mentor and guide junior data scientists, fostering technical excellence and continuous learning within the team
Skills
- 15 - 18 years of experience in advanced data science roles, with extensive leadership in designing and deploying statistical and machine learning solutions
- Expertise in hypothesis testing, including T-Test and Z-Test methodologies
- Advanced proficiency in regression techniques (linear and logistic)
- Strong programming skills in Python, PySpark, and R/R Studio
- Hands-on experience with SAS and SPSS for statistical analysis and computing
- In-depth knowledge of probabilistic graph models
- Experience with forecasting methods such as Exponential Smoothing, ARIMA, and ARIMAX
- Practical use of classification algorithms including Decision Trees and Support Vector Machines (SVM)
- Proficiency with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet
- Familiarity with distance metrics (Hamming, Euclidean, Manhattan)
- Working knowledge of Kubeflow and BentoML for model deployment and orchestration
- Master's or PhD degree in Data Science, Statistics, Computer Science, Mathematics, or a related field
- Relevant certifications in machine learning, statistical analysis, or advanced data science
- Experience implementing advanced model monitoring with Evidently AI
- Expertise in data pipeline automation and orchestration using Kubeflow
- Knowledge of emerging ML frameworks and architectures
- Experience with large-scale distributed computing environments
- Strong background in statistical validation and reproducibility best practices
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