Principal Data Scientist - R01566418

About Brillio:

Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction. Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.

Job 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

Key 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

Required Skills:

• 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

Preferred Skills:

• 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

Desired Qualifications:

• 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

Salary: 185,000-190,000 USD per year salary

Brillio is an equal opportunity employer to all, regardless of age, ancestry, colour, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (includes pregnancy, childbirth, breastfeeding, and related medical conditions), and sexual orientation.

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