At Apple, we believe in the power of technology to enrich people’s lives. Do you want to make an impact on how decisions are made by Engineering teams? If you love using statistical methods and analysis to influence products design and offerings’ decisions, then look no further! This Data Science team in the Apple Services Engineering organization provides insights through data that drive decision-making for our engineering and product teams. We are looking for a Senior Data Scientist who can work with big data, design and conduct AB tests, perform exploratory analysis, derive and communicate valuable insights, collaborate with product managers, engineers, and other x-functional teams to understand business requirements and translate them into analytical solutions. This is a high impact role within the experimentation organization. The candidate for this role must have proven expertise in statistical experimentations. This role will be working daily with researchers, engineers and product teams in areas like: UX, Search, Recommendations and others with goals of maintaining and improving our customer experience across App Store, Apple Music, TV and other services.

Partner closely with engineering, product, and machine learning teams. Work falls broadly into these areas:

Experimentation: Improve quality of experimentation planning, design and analysis, use advanced techniques and processes to address statistical challenges and accelerate testing.

Metrics design: translate business requirements into analytical solutions, explore, validate, standardize and automate pipelines to increase coverage for appropriate KPIs in reporting tools.

Ad-hoc analysis to develop customer and product knowledge.

Presenting and communicating complex analytical concepts and findings in a clear and concise manner to stakeholders at all levels of the organization.

6+ years of relevant industry experienceMaster of Science degree in Data Science, Biostatistics, Statistics, Computer Science, related engineering fieldMust have: extensive background and proven expertise in statistical experimentation methods, such as AB testing, experimental design, power analysis and non-parametric statistics.Proficiency in: SQL, Spark, Python/R/ScalaDeep understanding of common data science toolkits, such as pandas, NumPy, dplyr, etc.Experience using data visualization tools, such as Tableau, GGplot, matplotlib, seaborn, etc.Great communication skills and the ability to explain findings and concepts in layperson terms to key decision makersProven track record of working openly and collaboratively in x-functional environment and lead multiple projects simultaneously