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.

What you’ll do

• 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 workstreams 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 stakeholders.
• Guide senior Accenture leaders and client executives on AI strategy, adoption, governance, risk, regulation, and emerging technology.
• Engage with industry, policy, standards, regulatory, academic, and ecosystem stakeholders where appropriate.
• Develop and present Accenture perspectives, methodologies, accelerators, research, and thought leadership on AI and Responsible AI.
• Mentor data scientists and other practitioners by contributing reusable frameworks, standards, assets, accelerators, and communities of practice.
• Support clients with AI strategy, capability development, technology selection, organizational change, workforce adoption, and responsible scaling of AI.

Core responsibilities success factors

• Business value generated by AI and data‑science solutions.
• Quality, accuracy, reliability, robustness, adoption, and production performance of deployed AI systems.
• Effective identification and mitigation of AI‑related risks.
• Compliance with applicable Responsible AI policies, governance requirements, standards, and regulatory obligations.
• Successful implementation and adoption of AI‑governance operating models, processes, controls, and assurance mechanisms.
• Reduction in operational cost, cycle time, risk exposure, or manual effort.
• Improvement in customer, employee, citizen, or broader business outcomes.
• Scalability and reusability of AI architectures, methodologies, governance frameworks, and accelerators.
• Successful delivery of client engagements and workstreams against agreed outcomes.
• Contribution to client relationships, proposals, business development, and market‑facing thought leadership.
• Ability to influence senior client and Accenture stakeholders on AI strategy, Responsible AI, risk, and governance.
• Development, mentoring, and growth of data science and AI talent.

Requirements

• Minimum 6 years of relevant professional experience across data science, artificial intelligence, advanced analytics, Responsible AI, technology consulting, AI governance, or related disciplines.
• Bachelor’s or Master’s degree in a quantitative/technical discipline.
• Significant experience applying data science, machine learning, advanced analytics, or AI to real‑world business problems.
• Strong understanding of probability, statistics, experimental design, optimization, machine learning theory, and quantitative problem solving.
• Proficiency in Python and data science/ML libraries such as pandas , NumPy , scikit‑learn , PyTorch , TensorFlow , XGBoost , or equivalents.
• Experience designing, developing, validating, deploying, and monitoring machine learning models in production environments.
• Practical experience with generative AI including large language models, foundation models, prompt engineering, embeddings, semantic search, retrieval‑augmented generation, and model evaluation.
• Experience working with structured, semi‑structured, and unstructured data including textual, image, multimodal, transactional, or time‑series datasets.
• Strong SQL skills and experience working with modern data platforms, distributed‑processing technologies, cloud platforms, and enterprise data environments.
• Knowledge of software engineering practices including APIs, version control, automated testing, containerization, CI/CD, and production observability.
• Experience with AI governance and risk disciplines such as Responsible AI, model risk, data ethics, privacy, security, compliance, or related risk management.
• Working knowledge of AI‑related policy, standards, regulation, regulatory guidance, or assurance approaches.
• Ability to translate regulatory, ethical, policy, or risk requirements into practical governance processes, operating models, controls, and technology requirements.
• Strong client‑facing consulting skills including structured problem solving, executive communication, stakeholder management, workshop facilitation, and storytelling.
• Experience shaping and delivering complex projects or workstreams involving multidisciplinary teams.
• Strong written and verbal communication skills, with ability to explain complex technical, regulatory, and risk topics to senior stakeholders.

Technologies

• Python, pandas, NumPy, scikit‑learn, PyTorch, TensorFlow, XGBoost
• SQL
• MLOps, GenAIOps, LLMOps
• APIs, containerization, continuous integration, continuous deployment
• AWS, Microsoft Azure, Google Cloud

Benefits

• Medical, dental, vision coverage
• Life and long‑term disability coverage
• 401(k) plan
• Bonus opportunities
• Paid holidays
• Paid time off

Priority skills and knowledge

• Responsible AI and AI governance
• AI regulation, policy, standards, and compliance
• Generative AI and Agentic AI
• Data and AI ethics
• AI risk assessment and assurance
• AI governance operating models
• Governance structures, policies, standards, and controls
• Model and AI‑system evaluation
• Stakeholder and executive management
• Management consulting
• Project and workstream leadership
• Technology strategy and transformation

Bonus points if you have

• A doctorate in a quantitative, technical, or closely related discipline.
• Experience designing or deploying agentic AI systems, including tool‑using models, orchestration frameworks, workflow automation, reasoning systems, or multi‑agent architectures.
• Experience with knowledge graphs, graph analytics, causal inference, reinforcement learning, simulation, operations research, or mathematical optimization.
• Familiarity with vector databases, model gateways, model registries, feature stores, evaluation platforms, AI observability tools, and AI‑control technologies.
• Experience with major cloud and AI platforms such as AWS, Microsoft Azure, or Google Cloud.
• Deep knowledge of AI governance, data privacy, cybersecurity, model risk management, algorithmic accountability, or emerging AI regulation and standards.
• Experience developing AI risk‑taxonomy, AI inventory, impact‑assessment, control‑testing, assurance, or monitoring frameworks.
• Experience leading multidisciplinary teams or delivering enterprise‑wide AI, data, governance, risk, or technology‑transformation programs.
• Published academic research, industry papers, white papers, standards contributions, patents, or other recognized thought leadership in Responsible AI, AI governance, AI policy, AI ethics, or related fields.
• Experience engaging with regulators, standards bodies, policymakers, industry associations, or academic institutions.
• Ability to independently lead complex client workstreams from problem definition through implementation.
• Experience managing resources and stakeholders within a matrixed global organization.

Salary range: USD 87,400 - 293,800 per year.

Location: Culver City, CA (onsite).