Note: The job is a remote job and is open to candidates in USA. Brillio is a digital technology services provider focused on digital adoption and innovation. The Principal Data Scientist will design and deploy statistical and machine learning solutions, lead end-to-end data science projects, optimize data pipelines, and establish model evaluation and monitoring processes. The role also collaborates with cross-functional teams and mentors junior data scientists.
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
- 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
Benefits
- Remote work arrangement
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