[Remote] Senior Data Science Lead - R01566414

Note: The job is a remote job and is open to candidates in USA. Brillio is a fast-growing digital technology service provider that partners with Fortune 1000 companies to leverage digital adoption for competitive advantage. They are seeking a Senior Data Science Lead to design and implement complex data science solutions, mentor junior data scientists, and drive innovation through advanced statistical modeling and machine learning.

Responsibilities

• Lead the design and implementation of complex data science solutions to drive business impact and inform strategic decision-making
• Develop, validate, and optimize advanced statistical and machine learning models, including regression, classification, and forecasting algorithms
• Collaborate with cross-functional teams to translate business objectives into actionable analytics projects and deliver measurable outcomes
• Mentor and guide junior data scientists, fostering a culture of technical excellence and continuous learning
• Leverage Python, R, and relevant frameworks to build scalable data pipelines and automate model deployment using tools such as KubeFlow and BentoML
• Conduct rigorous statistical analysis, including hypothesis testing, T-Test, Z-Test, and probabilistic graph modeling to uncover actionable insights
• Implement and monitor model validation, explainability, and performance tracking using tools like Great Expectation and Evidently AI
• Stay current with emerging trends in machine learning, artificial intelligence, and big data technologies to drive innovation within the team

Skills

• Experience Range: 12+ years of experience in data science, including hands-on expertise in advanced statistical modeling and machine learning
• Expertise in hypothesis testing, T-Test, and Z-Test
• Advanced proficiency in regression techniques (linear and logistic)
• Strong programming skills in Python and PySpark
• Experience with SAS or SPSS for statistical analysis and computing
• Hands-on knowledge of probabilistic graph models
• Proficiency with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, or MXNet
• Forecasting techniques, including exponential smoothing, ARIMA, and ARIMAX
• Experience with model deployment tools such as KubeFlow and BentoML
• Strong understanding of classification algorithms (decision trees, SVM)
• Proficiency in R and R Studio
• Master's or PhD in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field
• Relevant certifications in machine learning, data science, or analytics (e.g., TensorFlow, SAS, or equivalent)
• Experience with Great Expectation and Evidently AI for model validation and monitoring
• Knowledge of advanced distance metrics (Hamming, Euclidean, Manhattan)
• Expertise in scalable data engineering for machine learning pipelines
• Hands-on experience with cloud-based machine learning platforms
• Familiarity with MLOps best practices and CI/CD for data science

Company Overview

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