Commercial Data Scientist, ANZSA

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Apple’s Channel Sales organisation works with the ecosystem of telecom operators and resellers that bring our products to customers around the world. Our teams collaborate with our partners to ensure they deliver the best customer experience when merchandising, demoing and selling Apple products that enrich the lives of hundreds of millions of people.

The Channel Sales organisation in ANZSA (Australia, New Zealand and South Asia) is looking for an outstanding data scientist to design and implement the next generation of AI-powered analytical solutions to support our growth.

This is a hands-on role for a builder. You’ll architect and implement transformative AI solutions leveraging data at scale. You’ll engage closely with various departments-such as sales, engineering, analytics, and other business units-to deliver AI-driven solutions that bolster our regional sales strategies. This role demands expertise in a wide array of modelling techniques, ranging from ML optimisation to LLM and agentic systems, all applied to address complex, high-stakes problems.

Description

- Bridge data science and business, with the ability to speak the language of both.

Collaborate with business leaders and cross-functional stakeholders to proactively identify business opportunities and translate complex business problems into well-defined analytical requirements.

- Perform exploratory data analysis (EDA) and formulate hypothesis to uncover business opportunities or challenges.

- Design and deploy statistical models to simulate potential outcomes for critical business topics, such as affordability, coverage, competitive landscape, and price elasticity.

- 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.

- Partner with engineering teams to productionalise models and solutions.

- Define and implement robust validation strategies to ensure model accuracy, reliability, and generalisability, leveraging both quantitative metrics and qualitative insights.

- Collaborate with data engineering teams to build and maintain robust data pipelines and deploy high-performance scalable models in production.

- Collaborate closely with Worldwide (WW) teams to localise global AI tools and other emerging solutions, ensuring their effectiveness and relevance within ANZSA.

- Act as the ANZSA subject matter expert for global AI initiatives and facilitating knowledge transfer within the local team.

- Keep up-to-date with the latest industry trends and technologies to ensure work remains cutting-edge and propose continuous improvement of AI platforms.

Minimum Qualifications

8+ years of professional experience in ML, AI, Data Science, or related fields, with a proven track record of delivering impactful ML solutions in industry settings.

Advanced degree in Computer Science, Data Science, or a related field, or equivalent professional experience.

Track record of collaborating with distributed engineering or data science teams to deliver business value.

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.

Strong ability to write complex SQL queries to extract, transform, and analyze large datasets from relational databases.

Extensive command of statistical modeling and causal inference, including measurement science, experimental design, and hypothesis testing to translate complex data into clear, actionable recommendations for business growth.

Exceptional communication and leadership skills, with a proven ability to translate undefined business questions into end-to-end data solutions, and articulate complex analyses clearly to executive stakeholders.

Demonstrated experience collaborating closely with business teams to deep dive into business performance and translate into solutions.

Demonstrated strengths in building and managing relationships, with the ability to influence at all levels, both within an organisation and externally.

Preferred Qualifications

Familiarity with large-scale data platforms (e.g. Snowflake), and with distributed processing frameworks (e.g. Spark, or Ray).

Knowledge of sales and customer engagement processes.

Ph.D. in Machine Learning, Computer Science, Mathematics, or a related field.