Data Science Lead - R01547299

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.
Data Science Lead

Primary Skills

• Problem Formulation (Business problem to Data Science Problem), OKR Validation against statistical measures, Data Wrangling, Data Storytelling & Insight Generation, Problem Solving, Excel VBA, Data Curiosity, Technical Decision Making (How many iterations to go for vs when to stop iterating), Communication & Articulation: Vocal & Written, Business Acumen (Consume new domains quickly to learn through data), Design Thinking, Data Literacy

Job requirements

• Role: Data Science Lead
• Location: Pleasanton, CA 
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• Job Description:
• 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 Job Description:  We are seeking an experienced Data Science Lead to spearhead the development, deployment, and scaling of Large Language Models (LLMs) within our AI ecosystem. This role requires a hands-on leader with deep expertise in LLM operationalization, prompt engineering, Databricks, and Azure AI services. You will lead initiatives in fine-tuning models, Retrieval-Augmented Generation (RAG), and transforming LLM prototypes into production-grade solutions that drive business impact.  
• Key Responsibilities:  
• Lead and scale LLM-based projects from proof-of-concept (PoC) to full-scale production across the organization, focusing on delivering measurable business value.  
• Productionalize LLMs on Azure, ensuring that models are robust, efficient, and scalable in live environments. 
• Develop and optimize prompt engineering techniques to enhance model performance, reduce costs, and improve the accuracy of outputs. 
• Collaborate with data engineers and architects to integrate LLMs with data pipelines on platforms such as Databricks for scalable processing. 
• Implement Retrieval-Augmented Generation (RAG) methods to improve the relevance and accuracy of LLM outputs, leveraging both structured and unstructured data sources. 
• Oversee fine-tuning of pre-trained LLMs to create domain-specific models that address business challenges in various verticals. 
• Drive continuous improvement and experimentation with LLM models, staying at the forefront of the latest advancements in Generative AI. 
• Ensure model governance, monitoring, and AI/ML Ops practices for safe and reliable deployment in production environments. 
• Collaborate with cross-functional teams, including product managers, engineers, and other stakeholders, to define project requirements, timelines, and deliverables. 
• Provide mentorship and guidance to junior data scientists, fostering a culture of innovation and knowledge sharing. 
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• Required Skills and Qualifications:  
• Experience with LLMs and Generative AI: Deep knowledge of Large Language Models (GPT, BERT, etc.) and their deployment at scale. 
• Azure AI Expertise: Proven experience with Azure Machine Learning, Azure Cognitive Services, and other cloud-native AI solutions for deploying models. 
• Prompt Engineering: Strong experience in prompt tuning and engineering techniques to refine outputs of LLMs. 
• Databricks: Hands-on experience with Databricks for big data processing, model training, and inference at scale. 
• Retrieval-Augmented Generation (RAG): Proficiency in implementing RAG techniques to improve the quality and relevance of model outputs. 
• Fine-Tuning: Experience in fine-tuning pre-trained models to meet specific business or domain needs. 
• Strong understanding of AI/ML Ops for continuous