Senior Data Scientist

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Company Overview

A leading global technology and professional services organization is seeking an experienced Responsible AI leader to help organizations design, develop, operationalize, and govern enterprise-scale artificial intelligence solutions. Its Global Responsible AI team works with organizations to address the opportunities and risks associated with increasingly powerful and accessible AI technologies.

Key Responsibilities

• Partner with business, product, data, engineering, architecture, cybersecurity, legal, privacy, risk, compliance, and operations teams to identify and prioritize high-value AI opportunities.
• Translate complex business challenges into clearly defined analytics, machine learning, generative AI, agentic AI, and decision-science problems.
• Perform exploratory data analysis, statistical analysis, hypothesis testing, experimental design, feature engineering, predictive modeling, and optimization.
• Develop supervised and unsupervised machine learning solutions, including classification, regression, clustering, forecasting, recommendation, anomaly detection, and optimization.
• Design and implement deep-learning solutions using neural networks, transformers, convolutional architectures, sequence models, representation learning, and multimodal approaches.
• Develop 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 incorporating reasoning, planning, memory, tools, workflows, human oversight, and single- or multi-agent orchestration.
• 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.

Required Qualifications

• At least 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 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 text, image, multimodal, transactional, and time-series datasets.
• Strong SQL skills and experience 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 policies, standards, regulations, regulatory guidance, or assurance approaches.
• Experience translating regulatory, ethical, policy, or risk requirements into practical governance processes, operating models, controls, and technology requirements.

Disclaimer

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