Data Scientist

Senior Data Scientist – AI / Machine LearningLocation: Bay Area, CA (Open/Hybrid)Role OverviewWe are seeking a Senior Data Scientist with deep expertise in Artificial Intelligence (AI) and Machine Learning (ML) to design, build, and deploy advanced data-driven and AI-powered solutions. This role requires strong hands-on experience across the full ML lifecycle—from problem framing and data engineering through model development, deployment, and monitoring—along with the ability to work independently and lead complex initiatives.The ideal candidate combines strong statistical foundations, modern ML/GenAI capabilities, and production-grade engineering skills, and can partner effectively with business, engineering, and leadership stakeholders.Key ResponsibilitiesLead end-to-end development of AI/ML solutions, including data exploration, feature engineering, model training, evaluation, and deploymentDesign, develop, and optimize machine learning models such as regression, classification, clustering, NLP, and deep learning modelsBuild and deploy production-grade ML systems, ensuring scalability, performance, reliability, and cost efficiencyDevelop Generative AI solutions including LLM-based applications, prompt engineering, RAG pipelines, and agentic workflows (where applicable)Collaborate with data engineers to design and maintain robust data pipelines for structured and unstructured dataPerform model validation, experimentation, and performance monitoring, ensuring accuracy, fairness, and robustnessTranslate complex analytical findings into clear business insights and recommendations for senior stakeholdersMentor junior data scientists and provide technical leadership across projectsContribute to AI governance, MLOps/LLMOps standards, documentation, and best practicesPartner cross-functionally with product, engineering, and business teams to deliver measurable business outcomesRequired Qualifications9+ years of hands-on experience in Data Science, Machine Learning, or Applied AIStrong proficiency in Python and common data science libraries (NumPy, pandas, scikit-learn)Solid experience with ML frameworks such as PyTorch, TensorFlow, or Hugging FaceStrong understanding of statistics, probability, and experimental designExperience building and deploying models in cloud environments (AWS, Azure, or GCP)Hands-on experience with model deployment, monitoring, and MLOps tools (e.g., MLflow, CI/CD for ML)Experience working with large datasets, SQL, and modern data storesExcellent communication skills with the ability to explain technical concepts to non-technical audiencesPreferred / Nice-to-Have SkillsExperience with Generative AI and Large Language Models (LLMs)Hands-on knowledge of RAG architectures, vector databases, and unstructured data pipelinesFamiliarity with LLMOps, observability, and responsible AI practicesExperience in consulting or client-facing environmentsKnowledge of big data technologies (Spark, Databricks)Prior experience leading small teams or acting as a technical leadEducationBachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related fieldPhD is a plus but not required