Commercial Data Scientist, Anzsa

Apple Apple · Big Tech · Singapore · Sales and Business Development

This role focuses on designing and implementing AI-powered analytical solutions for channel sales growth in the ANZSA region. It involves architecting and deploying transformative AI solutions, including ML optimization, LLMs, and agentic systems, leveraging techniques like RLHF and GNNs. The role requires close collaboration with business and engineering teams, productionizing models, and ensuring robust validation and data pipelines.

What you'd actually do

  1. Collaborate with business leaders and cross-functional stakeholders to proactively identify business opportunities and translate complex business problems into well-defined analytical requirements.
  2. Design and deploy statistical models to simulate potential outcomes for critical business topics, such as affordability, coverage, competitive landscape, and price elasticity.
  3. Develop solutions that turn data into insights by leveraging state-of-the-art techniques, ranging from ML optimisation to Reinforcement Learning from Human Feedback (RLHF), Graph Neural Networks (GNN), and Generative AI.
  4. Partner with engineering teams to productionalise models and solutions.
  5. Define and implement robust validation strategies to ensure model accuracy, reliability, and generalisability, leveraging both quantitative metrics and qualitative insights.

Skills

Required

  • Python
  • PyTorch
  • Tensorflow
  • SQL
  • ML optimization
  • LLM
  • agentic systems
  • RLHF
  • GNN
  • Generative AI
  • statistical modeling
  • causal inference
  • experimental design
  • hypothesis testing

Nice to have

  • Snowflake
  • Spark
  • Ray
  • sales and customer engagement processes

What the JD emphasized

  • proven track record of delivering impactful ML solutions in industry settings
  • Deep understanding of ML principles, including supervised/unsupervised learning and deep learning, with specific expertise in advanced techniques such as RLHF, GNNs or GenAI.
  • Expert proficiency in Python and standard ML libraries (e.g. PyTorch, Tensorflow), with experience writing clean, production-grade code.

Other signals

  • AI-powered analytical solutions
  • architect and implement transformative AI solutions
  • ML optimisation to LLM and agentic systems
  • state-of-the-art techniques, ranging from ML optimisation to Reinforcement Learning from Human Feedback (RLHF), Graph Neural Networks (GNN), and Generative AI