SummaryPosted: Dec 10, 2024Weekly Hours: 40 Role Number:200582295Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn’t have imagined, and now, can’t imagine living without. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do.DescriptionDescriptionAPPLE INC has the following available in Austin, Texas. Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Support the Marketing team with analytics and customer insights as it relates to ASA marketing campaign effectiveness of our email, webinar, ASA developer Certification program, paid media, and marketing web page enhancements. Empower the Marketing team with insights including campaign and program effectiveness that inform and fulfill strategic objectives and goals such as customer engagement with ASA marketing, marketing-influenced lifts in revenue and ASA account optimizations, and certification program adoption metrics, for every fiscal quarter. Quantify the impact of marketing initiatives on customer success and future behavior via the use of automated SQL-scripted transactional reports post-marketing, use of machine learning classification via Python to create customer segments, use of causal-inference frameworks via Python to determine if observed data of marketing efforts “caused” an effect revenue/product adoption metrics. Responsible for business analytics work through all phases, including: validation of data quality (substantial data sample sizes, adherence to privacy policy, outlier detection); execution of data analysis (automation of SQL scripts into Snowflake environment via usage of DBT, development in Python to conduct ML-based classification of customer segmentation, correlation of customer revenue and performance to ASA marketing efforts, revenue uplift/product engagement pre vs post ASA marketing campaigns, causal-inference analysis to prove causation of efforts, forecasting future customer engagement and adoption); development of data visualization (using Tableau to create selfservice dashboards and reports to be consumed by marketing stakeholders that develop content and strategy); presentation of results and deliverables (using Keynote to generate charts, visuals and diagrams that share the AP Marketing Insights & Analytics team’s findings to broader cross-functional audiences in a presentation setting). Monitor usage metrics of ASA product features and placements, understanding business-based explanations for large scale trends and patterns in customer lifecycle behavior. Collect, analyze and interpret advertising campaign performance and transactional data via SQL scripts querying datasets in Snowflake data warehouse environment with data exploration and analysis conducted in Jupyter notebooks/labs via Python, Excel and Tableau. Develop data models and analytical tools for ongoing audience segmentation, benchmarking, trending and competitive analysis. Build dashboards, reports and presentations of findings in Tableau, Jupyter notebooks and/or Keynote to service cross-functional partners with insights. Collaborate with cross functional teams, including Data Insights, Operations, Product, Finance and Engineering to gain access to unique datasets, validate analyses, automate outputs of analyses into a shared Snowflake data warehouse environment, and ensure alignment of projects with Ad Platforms broader business objectives. Design, conduct and analyze A/B tests to optimize performance of marketing campaigns. Develop and maintain customer engagement propensity models with Python, using classification techniques to identify the strongest features in marketing outreach. Conduct full audit of pre-existing queries in Hive environments and create automated Python scripts to convert queries for new database structure in Snowflake. 40 hours/week. PAY & BENEFITS: Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits: https://www.apple.com/careers/us/benefits.html. Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.Minimum QualificationsMinimum QualificationsMaster’s degree or foreign equivalent in Business Administration, Statistics, Math, Finance, Economics or related field and 3 years of experience in the job offered or related occupation.3 years of experience with each of the following skills is required:Developing code for data science and data analyses, creating visualizations to present and monitor the model performance.Writing scripts for Machine Learning and statistical models in Jupyter Notebook via Python for classification and regression model development and model validation.Programming in R to develop models that are used for CCAR (Comprehensive Capital Analysis and Review) model development, clustering, stress testing and scenario analysis.Using SQL to select, transform and load into data science models and visualization tools.Developing data visualizations, dashboards and reporting in Tableau for stakeholders to consume.Using Excel for data analysis and data exploration processes of analysis. Using VLOOKUP to match rows with relevant data for further analyses and pivot table exploration for high-level diagnostics of an output dataset in csv or xlsx format.Exploratory data analysis and advanced quantitative analysis using data science Machine Learning models. Using Jupyter notebook via Python to explore dataset imbalances, missing or NULL values and other inconsistencies, determining feature importance, evaluating ML models via classification reports and evaluation metrics.Key QualificationsKey QualificationsPreferred QualificationsPreferred QualificationsN/AEducation & ExperienceEducation & ExperienceAdditional RequirementsAdditional RequirementsMore

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