Senior Data Scientist

Accenture is looking for a Senior Data Scientist to join its Global Responsible AI team in Culver City, CA. In this onsite role, you will help design and operationalize enterprise-scale AI solutions with Responsible AI governance, combining hands‑on machine learning work with policy‑aware risk management and client advisory.

• Partner with business, product, data, engineering, architecture, cybersecurity, legal, privacy, risk, compliance, and operations teams to identify, assess, and prioritize high-value AI opportunities.
• Translate complex business challenges into analytics, machine learning, generative AI, agentic AI, and decision‑science problem statements.
• Conduct exploratory and statistical analysis, hypothesis testing, experimental design, feature engineering, predictive modeling, and optimization.
• Develop supervised and unsupervised machine learning solutions across use cases such as classification, regression, clustering, forecasting, recommendation, anomaly detection, and optimization.
• Build deep‑learning solutions using neural networks and architectures including transformers, convolutional models, sequence models, representation learning, and multimodal approaches.
• Create NLP and computer vision solutions for document intelligence, information extraction, semantic search, knowledge discovery, image analysis, and multimodal understanding.
• Develop generative AI applications with large language models and foundation models, including prompt engineering, embeddings, vector search, retrieval‑augmented generation, fine‑tuning, model adaptation, guardrails, and evaluation.
• Design agentic AI solutions that integrate reasoning, planning, memory, tools, workflows, human oversight, and single‑or‑multi‑agent orchestration to support complex business processes.
• Evaluate commercial, open‑source, and internally developed AI models and platforms for performance, accuracy, robustness, cost, latency, scalability, security, privacy, explainability, maintainability, and operational fit.
• Design experimentation frameworks, evaluation methodologies, benchmarks, test datasets, acceptance criteria, and performance metrics for traditional, generative, and agentic AI systems.
• Work with data engineers, software engineers, machine learning engineers, architects, cybersecurity specialists, and platform teams to operationalize scalable AI solutions using MLOps
  , GenAIOps
  , and LLMOps practices.
• Establish monitoring and observability for model performance, drift, bias, fairness, hallucination, toxicity, safety, latency, cost, resilience, and overall system reliability.
• Assess AI use cases and systems for risk across fairness, transparency, explainability, privacy, security, robustness, human oversight, accountability, and regulatory compliance.
• Design and implement Responsible AI operating models including governance structures, policies, standards, controls, risk‑assessment methodologies, assurance processes, and supporting technology capabilities.
• Advise clients on emerging AI legislation, regulation, standards, regulatory guidance, and industry practices, while tracking major developments and translating them into actionable guidance.
• Support organizations in establishing AI inventories, classification and risk‑tiering approaches, governance workflows, control libraries, documentation standards, testing frameworks, and ongoing monitoring.
• Act as a subject matter expert in Responsible AI across broader data, AI, cloud, digital, and enterprise‑transformation programs.
• Shape and lead Responsible AI and AI‑governance engagements from initial assessment and strategy through design, implementation, operationalization, and continuous improvement.
• Engage in prospective client discussions, identify opportunities, shape solutions, develop proposals, and support sales conversations tied to AI, Generative AI, Agentic AI, and Responsible AI.
• Lead client work streams and multidisciplinary delivery teams, managing scope, outcomes, risks, dependencies, stakeholders, and delivery quality.
• Communicate analytical findings, AI‑system behavior, limitations, risks, trade‑offs, and business implications to both technical and non‑technical…