Principal Data Scientist - R01566418

Brillio•Edison, NJ

About The Position

Brillio is seeking a Principal Data Scientist to join their team. This role involves designing and deploying statistical and machine learning solutions for Fortune 1000 companies. The ideal candidate will have extensive leadership experience in advanced data science and a passion for leveraging cutting-edge digital technologies. Brillio is recognized as a Great Place to Work®, emphasizing innovation, client satisfaction, and professional growth for its employees, known as "Brillians".

Requirements

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

Nice To Haves

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

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