[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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