Senior Applied Scientist, Responsible AI – X Delivery

BCG BCG · Consulting · Singapore +1 · Technology and Engineering

The Senior Applied Scientist, Responsible AI will focus on developing and applying innovative tools for testing and evaluating GenAI products. This role involves designing and implementing testing and evaluation frameworks to improve product quality and ensure AI systems are safe, secure, and equitable. The scientist will research emerging threats and measurement approaches, collaborate with product teams to influence risk mitigation, and train technical practitioners on evaluation methods for GenAI products.

What you'd actually do

  1. Develop tools and techniques to scale and accelerate AI risk assessment and measurement across BCG and our clients
  2. Collaborate with product teams to influence quality and risk measurement and mitigations in AI/GenAI products through manual efforts and scalable, quantitative approaches
  3. Research new and emerging threats and measurement/evaluation approaches to ensure our approaches stay on the cutting-edge
  4. Work with small technical teams executing risk assessment and measurement on AI/GenAI products
  5. Train and mentor technical practitioners on measurements and evaluation approaches for GenAI products

Skills

Required

  • Design and analysis of experiments
  • Statistical modeling, including hierarchical linear models
  • GenAI, including prompt engineering and programmatically interacting with foundation models through APIs, e.g., OpenAI, Anthropic, HuggingFace
  • Python and the open-source data science ecosystem (e.g., Jupyter, pandas, scikit-learn, statsmodels, plotly, etc.)
  • Version control with git
  • Ability to proactively identify ethical, social, and business risks posed by AI systems
  • Experience designing and analyzing experiments using advanced statistical methods
  • Experience and enthusiasm for working with Generative AI technologies
  • Broad conceptual understanding of ML and AI paradigms (e.g., tree-based and gradient-boosted models, deep learning)
  • Excellent written, verbal, and visual communication skills
  • Ability to explain sophisticated data science concepts to non-technical audiences and translate analytical results into business implications

Nice to have

  • Familiarity with software engineering practices (e.g., unit testing, CI/CD)
  • Exposure to cloud platforms (AWS, Azure, GCP) or SQL databases
  • Knowledge of AI risk management frameworks e.g., NIST AI RMF, ISO 42001)

What the JD emphasized

  • Responsible AI policy
  • Responsible AI program
  • Responsible AI team
  • Responsible AI Applied Scientist

Other signals

  • Responsible AI
  • testing and evaluation frameworks
  • AI risk assessment
  • GenAI products