Senior Data Science Lead - R01566414

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. Senior Data Science Lead Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Data Science Advanced: Data Specialist Job Requirements Experience Range: 12+ years of experience in data science, including hands‑on expertise in advanced statistical modeling and machine learning. Work Type: Hybrid min 3 days onsite per week Key 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. Required Skills 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. Preferred Skills 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. Desired Qualifications 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). #J-18808-Ljbffr Brillio