At Apple, the business problem is to improve the customer experience and help advertisers grow their businesses through Apple Ads, by developing the next generation of analytical solutions and leveraging large language models and data science to drive business decisions.
Requirements
- Experience in statistical analysis, machine learning models, and advanced quantitative methods with a strong focus in causal inference.
- 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.
- Comfortable with a variety of data stores such as Hadoop, and Snowflake, familiar with distributed analytics engines such as Spark/PySpark.
- Familiarity of Causal Inference packages such as CausalImpact, DoubleML, DoWhy, and EconML.
- Familiarity with job orchestration frameworks such as Airflow.
Responsibilities
- 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.
Other
- 3+ years of recent experience in a data science role.
- Bachelor's degree in a related field of study, or equivalent industry experience.
- 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.
