Accenture’s Global Responsible AI team within the Global Data & AI Practice builds enterprise-scale AI solutions that are designed for performance and governed for risk. In this Senior Data Scientist role, you will help translate Responsible AI requirements and regulatory expectations into practical, end-to-end outcomes across the AI lifecycle, working closely with client and cross-functional teams.
What you will do
- Partner with business, product, data, engineering, architecture, cybersecurity, legal, privacy, risk, compliance, and operations stakeholders to identify, assess, and prioritize high-value AI opportunities.
- Convert complex business challenges into well-defined analytics, machine learning, generative AI, agentic AI, and decision-science problem statements.
- Apply exploratory data analysis, statistical analysis, hypothesis testing, and experimental design, including feature engineering, predictive modeling, and optimization.
- Develop supervised and unsupervised machine learning solutions for tasks such as classification, regression, clustering, forecasting, recommendation, anomaly detection, and related techniques.
- Design and implement deep-learning approaches using neural networks and architectures including transformers, convolutional models, sequence models, representation learning, and multimodal methods.
- Build natural language processing and computer vision solutions for document intelligence, information extraction, semantic search, knowledge discovery, image analysis, and multimodal understanding.
- Create generative AI applications with large language models and foundation models, covering prompt engineering, embeddings, vector search, retrieval-augmented generation, fine-tuning, model adaptation, guardrails, and evaluation.
- Develop agentic AI solutions that combine reasoning, planning, memory, tools, workflows, human oversight, and single- or multi-agent orchestration for complex processes.
- Evaluate AI models and platforms, including commercial, open-source, and internal solutions, across 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.
- Operationalize scalable AI solutions with MLOps, GenAIOps, and LLMOps by collaborating with engineering and platform teams.
- Establish monitoring and observability for model performance and reliability, including drift, bias, fairness, hallucination, toxicity, safety, latency, cost, resilience, and 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, governance structures, policies, standards, controls, risk-assessment methodologies, assurance processes, and enabling technology capabilities.
- Advise clients on emerging AI legislation, regulation, standards, regulatory guidance, and industry practices; maintain awareness of changes and translate them into actionable guidance.
- Support implementation of AI inventories, classification and risk-tiering approaches, governance workflows, control libraries, documentation standards, testing frameworks, and ongoing monitoring.
- Serve as a subject matter expert in Responsible AI across broader data, AI, cloud, digital, and enterprise-transformation programs.
- Lead Responsible AI and AI-governance engagements from assessment and strategy through design, implementation, operationalization, and continuous improvement.
- Engage in client meetings and prospective work 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 and AI-system behavior, including limitations, risks, and trade-offs, to technical and non-technical stakeholders.
- Provide guidance to senior Accenture leaders and client executives on AI strategy, adoption, governance, risk, regulation, and emerging technology.
- Participate with relevant ecosystem stakeholders when appropriate, and develop Accenture perspectives, methodologies, accelerators, research, and thought leadership.
- Mentor data scientists and practitioners, 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.
Skills and experience
- At least 6 years of relevant professional experience across data science, AI, advanced analytics, Responsible AI, technology consulting, AI governance, or related disciplines.
- A Bachelor’s or Master’s degree in a quantitative or technical field (data science, statistics, mathematics, computer science, engineering, economics, operations research, or similar).
- Significant experience applying data science, machine learning, advanced analytics, or AI to real-world business problems.
- Strong foundation in probability, statistics, experimental design, optimization, machine learning theory, and quantitative problem solving.
- Proficiency in Python and common libraries including pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent.
- Experience designing, developing, validating, deploying, and monitoring machine learning models in production.
- Practical generative AI experience with large language models and foundation models, including prompt engineering, embeddings, semantic search, retrieval-augmented generation, and model evaluation.
- Experience with structured, semi-structured, and unstructured data including textual, image, multimodal, transactional, or time-series datasets.
- Strong SQL skills and experience with modern data platforms, distributed processing, cloud platforms, and enterprise data environments.
- Understanding of software engineering practices such as APIs, version control, automated testing, containerization, continuous integration, continuous deployment, and production observability.
- Experience with AI governance and risk disciplines including Responsible AI, model risk, data ethics, privacy, security, compliance, or related areas.
- Knowledge of AI policy, standards, regulation, regulatory guidance, or assurance approaches.
- Ability to translate regulatory, ethical, policy, or risk requirements into 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 involving multidisciplinary teams.
- Strong written and verbal communication skills to explain complex technical, regulatory, and risk topics to senior stakeholders.
Technology exposure
This role involves work across a broad tooling landscape, including Python, pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, SQL, APIs, version control, automated testing, containerization, CI/CD, and MLOps, GenAIOps, and LLMOps. It also includes experience with large language models and foundation models, prompt engineering, embeddings, vector search, retrieval-augmented generation, fine-tuning, guardrails, transformers, deep learning architectures, natural language processing, computer vision, and agent orchestration with human oversight. Cloud and platform exposure may include AWS, Microsoft Azure, Google Cloud, along with elements such as vector databases, model gateways, model registries, feature stores, evaluation platforms, and AI observability and AI-control technologies.
Benefits
- Medical, dental, vision, life, and long-term disability coverage
- 401(k) plan
- Bonus opportunities
- Paid holidays
- Paid time off
Compensation and location
- Location: Carmel, IN (onsite)
- Salary: USD 87,400 - 293,800 per year
Travel
Travel may be required for this role; the amount of travel will vary from 0 to 100% depending on business need and client requirements.
How success will be measured
- 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
