Data Scientist

Apple•Austin, TX

11d•Onsite

About The Position

Serve as a Data Scientist on the Employee Productivity & Support team. Act as the technical lead for data science projects focused on improving internal IT support systems and employee productivity. Drive project execution from scoping to delivery and mentoring junior data scientists on advanced analytical methodologies and coding standards. Apply expertise in data wrangling to design and build data pipelines. Extract, clean, transform, and validate data from disparate company systems, including IT ticketing platforms, application logs, and multi-channel communication data (Slack, email, phone, chat), to create reliable, analysis-ready datasets. Develop, validate, and deploy production-level machine learning models, including regression, classification, clustering, time-series forecasting, and Natural Language Processing (NLP), to identify operational inefficiencies and drivers of employee satisfaction. Develop and implement statistical and time-series forecasting models to predict key business metrics, including IT support ticket volumes, performance trends, and staffing requirements, to enable proactive resource planning. Apply causal inference techniques to observational data to mitigate confounding and bias. Design, execute, and analyze controlled experiments (e.g., A/B tests) and other hypothesis tests to measure the impact of new tools and process changes on employee productivity. Integrate and apply Generative AI and Large Language Models (LLMs) to perform complex analytical tasks, including automated ticket classification, summarization of unstructured employee feedback from support interactions, and sentiment analysis. Utilize Git for version control to maintain well-documented, reproducible codebases in a collaborative environment. Perform code reviews and deliver reproducible analyses and reusable workflows based on established methodologies. Communicate complex analytical findings, statistical results, and model outputs to technical and non-technical audiences, including director-level leadership, through formal presentations, written reports, and the creation of business intelligence dashboards in Tableau. Create and maintain comprehensive technical documentation for data sources, analytical models, and codebases, including data dictionaries, model cards, and project summaries, to ensure collaboration, transparency, and reproducibility of results. 40 hours/week.

Requirements

Master’s degree or foreign equivalent in Business Analytics, Data Science, Statistics, Applied Mathematics, Operations Research, Economics, Natural Sciences, or a related field
3 years of experience in the job offered or related occupation.
2 years of experience using Python to perform data wrangling, cleaning, manipulation, data modeling, exploratory data analysis, visualization, and training machine learning models.
2 years of experience using Tableau or Power BI to perform exploratory data analysis and building interactive dashboards and reports by combining multiple data sources via joins, blends, and merges.
2 years of experience creating nested LOD calculations and parameters to allow users to interact with complex visualizations including Sankey charts, radar charts, or stories.
2 years of experience utilizing Excel to manipulate and conduct data validation, conduct exploratory data analysis and create collaborative templates including pivot tables, and v-lookups.
2 years of experience working with machine learning for predictive and explanatory modeling, including supervised and unsupervised techniques including SVM, linear and logistic regression, random forest, gradient boosted machines, hierarchal clustering, k-means, k nearest neighbors, and other algorithms.
2 years of experience performing statistical analysis to distinguish random processes from deterministic ones.
Specifically, 2 years of experience using hypothesis tests including t-test or ANOVA, linear models, and describing different distributions including normal, or Poisson to derive insights from data.
2 years of experience using SQL to create data models for visualization and analysis.
2 years of experience creating complex data models using chained common table expressions, window functions, and other advanced manipulations.
2 years of experience performing natural language processing including TF-IDF, word2vec, or BERT and recommendation systems including matrix factorization, or cosine similarity to manage text data and perform tasks including text classification, entity recognition, or sentiment analysis.

Responsibilities

Act as the technical lead for data science projects focused on improving internal IT support systems and employee productivity.
Drive project execution from scoping to delivery and mentoring junior data scientists on advanced analytical methodologies and coding standards.
Design and build data pipelines.
Extract, clean, transform, and validate data from disparate company systems, including IT ticketing platforms, application logs, and multi-channel communication data (Slack, email, phone, chat), to create reliable, analysis-ready datasets.
Develop, validate, and deploy production-level machine learning models, including regression, classification, clustering, time-series forecasting, and Natural Language Processing (NLP), to identify operational inefficiencies and drivers of employee satisfaction.
Develop and implement statistical and time-series forecasting models to predict key business metrics, including IT support ticket volumes, performance trends, and staffing requirements, to enable proactive resource planning.
Apply causal inference techniques to observational data to mitigate confounding and bias.
Design, execute, and analyze controlled experiments (e.g., A/B tests) and other hypothesis tests to measure the impact of new tools and process changes on employee productivity.
Integrate and apply Generative AI and Large Language Models (LLMs) to perform complex analytical tasks, including automated ticket classification, summarization of unstructured employee feedback from support interactions, and sentiment analysis.
Utilize Git for version control to maintain well-documented, reproducible codebases in a collaborative environment.
Perform code reviews and deliver reproducible analyses and reusable workflows based on established methodologies.
Communicate complex analytical findings, statistical results, and model outputs to technical and non-technical audiences, including director-level leadership, through formal presentations, written reports, and the creation of business intelligence dashboards in Tableau.
Create and maintain comprehensive technical documentation for data sources, analytical models, and codebases, including data dictionaries, model cards, and project summaries, to ensure collaboration, transparency, and reproducibility of results.