ML Data Scientist - Business Analytics

At Apple, we focus deeply on our customers' experience. Apple Ads brings this same approach to advertising, helping people find exactly what they're looking for and helping advertisers grow their businesses. Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact. We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes-from small app developers to big, global brands. Because when advertising is done right, it benefits everyone. The Apple Ads Data Insights team is seeking a dedicated Data Scientist who will innovate while developing the next generation of analytical solutions working multi-functionally with Sales, Marketing, Finance, Product, and Engineering. Analytics is a team sport, and in your role, you will be a key part of data-centric team that delivers insights that have direct and measurable impact. What makes this role exciting is the opportunity to shape how large language models and data science drive business decisions.

Description

Responsibilities include turning the huge amounts of data generated by user searches, app content, and App Store context into business insights that improve the customer experience for the end-user as well as drive discovery and efficiency for app developers. This role requires both a broad knowledge of statistics and creativity to invent and customize when vital. Dig in and get into the details. The theory behind the techniques are just the beginning. Work on projects where practical applications of these approaches get applied in real-world scenarios. Successful analytics teams involve data scientists and data engineers working hand in hand to build insightful and efficient solutions. We're looking for an inquisitive, collaborative, and passionate person to join this amazing team. Support Product, Engineering, and the Executive Team with analyses, and data products to improve product performance, deepen customer insight and deliver business impact.

• Empower the product and sales teams with insights to advise and fulfill their strategic objectives and goals.
• Monitor business metrics and identify key drivers for large scale trends and patterns in business health.
• Fine-tune, and evaluate large language models for internal and customer-facing use cases.
• Optimize prompt engineering and model performance for business-specific contexts.
• Build reusable, interpretable models to identify key drivers of business performance.
• Develop frameworks that allow teams to measure, visualize, and understand causal factors and business impact.
• Partner with business stakeholders to translate strategic questions into analytical models and measurable KPIs.

• 3+ years of recent experience in a data science role.
• Experience in statistical analysis, machine learning models, and advanced quantitative methods with a strong focus in causal inference. Must include experience with regression, classification, clustering, time-series analysis, and LLMs.
• Exceptional programming skills in Python and SQL. Comfort with advanced analytics and data visualization tools and libraries such as Pandas, R, Spark, and Tableau.
• Deep familiarity with commonly used Statistics and ML libraries such as ScikitLearn, SparkMLLib, SciPy, and/or StatsModels.
• Demonstrated experience in applying statistics and machine learning to generate clear actionable insights.
• Comfortable with a variety of data stores such as Hadoop, and Snowflake, familiar with distributed analytics engines such as Spark/PySpark.
• Posses exceptional communication skills to communicate analyses in a clear and effective manner to technical audience and executive leadership.
• Demonstrated ability to partner with engineering, meet the data needs of the business, finding creative analytical solutions and develop initial prototypes to address complex business problems.
• Demonstrated ability to operate comfortably and optimally in a fast-paced and constantly evolving environment.
• Bachelor's degree in a related field of study, or equivalent industry experience.

• Experience in the mobile advertising industry or related field.
• Familiarity of Causal Inference packages such as CausalImpact, DoubleML, DoWhy, and EconML.
• Familiarity with job orchestration frameworks such as Airflow.
• Demonstrated ability to build visualizations, dashboards and enable broader consumption of insights and tools for investigations.