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Description

APPLE 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 self-service 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.

Preferred Qualifications

N/A

Minimum Qualifications

Bachelor's Degree or foreign equivalent in Data Science, Mathematical Finance, Computer Science, Engineering, Physics, 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 and implementing techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software like SQL queries in Snowflake and Thrift environments.

Developing, validating, and deploying machine learning and statistical models in Python using Jupyter Notebooks and utilizing data preprocessing, feature engineering, model selection, model training, model testing, benchmarking and performance evaluation, cross-validation and ongoing monitoring.

Monitoring customer usage metrics of product features for large scale trends and patterns in customer lifecycle behavior through automated SQL queries and machine learning anomaly detection techniques.

Collaborating with cross-functional teams to gain access to unique datasets, validate analyses, to ensure alignment of projects with broader business objectives.

Developing data models in Python IDE environments & Jupyter notebook for producing outputs that flow downstream to analytical tools for ongoing audience segmentation, benchmarking, trending and competitive analysis.

Delivering data-driven insights through ad-hoc analyses for cross-functional stakeholders to define and measure key business KPIs using advanced statistical techniques and visualization tools such as Tableau and communicate findings through clear presentations tailored to technical and non-technical audiences, including executive leadership.

Interfacing with stakeholders to identify business-first actions and apply them to standardized and automated SQL queries for monitoring KPIs.

Designing and implementing customer segmentation models in Python and R using techniques such as Principal Component Analysis (PCA), K-Means clustering, and other unsupervised learning methods to evaluate segmentation quality and generate actionable insights to support targeted business strategies.

Quantifying the effectiveness of business initiatives on customer behavior and success using SQL and Python-based statistical and machine learning modeling.

Automating impact analyses through scripted transactional reporting pipelines and evaluating model effectiveness using appropriate accuracy and error metrics, as well as business performance metrics to support data-driven decision-making.

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