Senior Data Scientist

Accenture•St. Louis, MO

2d•$80,400 - $293,800•Hybrid

About The Position

Accenture’s Global Responsible AI team within the Global Data & AI Practice is seeking an experienced professional to design, develop, operationalize, and govern enterprise-scale artificial intelligence solutions. This role involves working with leading organizations to ensure AI is designed, built, and deployed in a manner that engenders trust and adheres to laws, regulations, and ethical norms. The team develops and deploys differentiated IP and Responsible AI solutions, engages with regulators, conducts research, and offers training through the Responsible AI Academy. The ideal candidate will combine technical depth, business acumen, consulting experience, experimentation discipline, regulatory awareness, and strong stakeholder leadership, comfortable moving between hands-on technical problem-solving, executive-level advisory, client delivery, business development, and thought leadership. The role involves working across industries and functional areas to guide clients through the entire AI lifecycle, from strategy and discovery through experimentation, engineering, deployment, governance, monitoring, and continuous improvement, while also contributing to Accenture’s perspectives on AI technologies, governance, and policy.

Requirements

A minimum of 6 years of relevant professional experience across data science, artificial intelligence, advanced analytics, Responsible AI, technology consulting, AI governance, or related disciplines.
A Bachelor’s or Master’s degree in data science, statistics, mathematics, computer science, engineering, economics, operations research, or another quantitative or technical discipline.
Significant experience applying data science, machine learning, advanced analytics, or artificial intelligence to real-world business problems.
Strong understanding of probability, statistics, experimental design, optimization, machine learning theory, and quantitative problem solving.
Proficiency in Python and commonly used data science and machine learning libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent technologies.
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.
Understanding of software engineering practices including APIs, version control, automated testing, containerization, continuous integration, continuous deployment, and production observability.
Experience with AI governance, Responsible AI, model risk, data ethics, privacy, security, compliance, or related risk-management disciplines.
Working knowledge of AI-related policy, standards, regulation, regulatory guidance, or assurance approaches.
Experience translating 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, including the ability to explain complex technical, regulatory, and risk topics to senior stakeholders.

Nice To Haves

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.

Responsibilities

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 clearly defined analytics, machine learning, generative AI, agentic AI, and decision-science problem statements.
Perform exploratory data analysis, statistical analysis, hypothesis testing, experimental design, feature engineering, predictive modelling, and optimization.
Develop supervised and unsupervised machine learning solutions, including classification, regression, clustering, forecasting, recommendation, anomaly detection, optimization, and related techniques.
Design and implement deep-learning solutions using neural networks, transformers, convolutional architectures, sequence models, representation-learning techniques, and multimodal approaches.
Build natural language processing and computer vision solutions for document intelligence, information extraction, semantic search, knowledge discovery, image analysis, and multimodal understanding.
Develop generative AI applications using 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 combine reasoning, planning, memory, tools, workflows, human oversight, and single- or multi-agent orchestration to execute complex business processes.
Evaluate commercial, open-source, and internally developed AI models and platforms based on 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.
Collaborate 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 areas including fairness, transparency, explainability, privacy, security, robustness, human oversight, accountability, and regulatory compliance.
Design and implement Responsible AI operating models, governance structures, policies, standards, controls, risk-assessment methodologies, assurance processes, and supporting technology capabilities.
Advise clients on the implications of emerging AI legislation, regulation, standards, regulatory guidance, and industry practices.
Maintain awareness of major developments in AI policy, regulation, technical standards, assurance, and governance and translate these developments into actionable guidance for clients.
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 within 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 with prospective clients to identify opportunities, shape solutions, develop proposals, and support sales conversations related 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.
Provide guidance to senior Accenture leaders and client executives on AI strategy, adoption, governance, risk, regulation, and emerging technology.
Engage with relevant 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 and contribute to 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.