Senior Data Scientist

Accenture; Culver City (CA); On-site; USD 87,000 - 294,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

401(k) plan

Bonus opportunities

Paid holidays

Paid time off

Job summary

Accenture is seeking a Senior Data Scientist to join the Global Responsible AI team in Culver City, CA. This onsite role combines hands-on ML with governance, risk management, and client advisory to design enterprise-scale AI solutions.

You will collaborate with cross‑functional teams to operationalize AI, develop ML models including NLP/vision, and advance responsible AI practices, governance, and compliance.

Qualifications

• 6+ years of relevant experience in data science, AI, or analytics.
• Experience applying ML/AI to real-world business problems.
• Strong understanding of statistics, experimental design, and optimization.
• Proficiency in Python and ML libraries.
• Experience deploying ML models in production environments.
• Knowledge of responsible AI, governance, and risk considerations.

Responsibilities

• Partner with business, product, data, and engineering teams to identify AI opportunities.
• Translate business challenges into analytics, ML, and AI problem statements.
• Develop ML solutions across classification, forecasting, NLP, and vision use cases.
• Build deep-learning models using transformers and sequence models.
• Design and implement Generative AI and agentic AI applications.
• Establish responsible AI governance, risk assessment, and monitoring frameworks.

Skills

Python

SQL

Machine Learning

Statistics

Data Visualization

Communication

Problem Solving

Education

Bachelor's or Master's in quantitative field

Tools

Pandas

NumPy

scikit-learn

PyTorch

TensorFlow

XGBoost

Job description

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).