2024 Machine Learning Engineer Intern

Allergan Data Labs is on a mission to transform the Allergan Aesthetics beauty business at Abbvie, one of the largest pharmaceutical companies in the world. Our iconic brands include Botox, CoolSculpting, Juvederm, and more. The medical aesthetics business is ripe for rapid growth and disruption, and we are looking for a Machine Learning Engineer Intern to add to our high performing team. 

Our team has successfully launched a new and innovative technology platform, Alle, which serves millions of consumers, tens of thousands of aesthetics providers, and thousands of colleagues throughout the US. Since its launch in November 2020, Alle has delivered curated promotions, personalized experiences, and had millions of consumers use it as part of their beauty journey. 

Allergan Data Labs is a vibrant startup-minded organization with the backing of a large company. You will be responsible for collaborating with cross functional partners and applying your data skills to deliver insights from data and build data-driven solutions for products, operations, marketing, and sales. 

As a Machine Learning Engineer Intern, you will report to the Manager of Data Engineering. Your role will span 10 to 12 weeks beginning in May 2024 and concluding in August 2024. 

You Will 

• Collaborate with Data Engineers, Software Engineers, and other business partners to identify, gather, cleanse, and organize data sets needed for Machine Learning and AI models.
• With guidance from seasoned Machine Learning Engineers & Data Scientists, write scalable code and build data pipelines to extract meaningful features from raw data.
• Assist seasoned Data Scientists to design, train, and evaluate Machine Learning and/or AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc.
• Collaborate with seasoned Machine Learning Engineers & Data Scientists to design and implement Machine Learning and/or AI models as robust solutions that can be deployed into production at scale as microservices, reverse ETLs, or stream processing.
• Collaborate with seasoned Machine Learning Engineers & Data Scientists to implement production monitoring metrics that detect performance degradations such as non-stationary behavior & anomalies, and that automatically trigger model retraining and/or alerts.
• Work with stakeholders to identify model performance criteria and implement production solutions to monitor performance.
• Follow our governance & development standards, including processes & frameworks for logging experiments, code & model quality standards, documentation, and source controlling artifacts.
• Clearly document & present your work and informational materials at the appropriate level of detail to your team & business partners. 

Required Experience & Skills 

• One semester or one year left and currently enrolled in a MS, or PhD program in Computer Science, Mathematics, Statistics, Engineering, Operations Research, or other quantitative field.
• Strong programming skills in Python, an understanding of core computer science principles, and experience with Python data manipulation frameworks such as Pandas & PySpark
• Knowledge of SQL and relational database design
• Broad knowledge of basic computational statistics and good understanding of theoretical fundamentals of statistics
• Thorough and broad knowledge of Machine Learning modeling & training techniques, as well as best practices for ensuring robustness & performance.
• Familiarity with practical data pipeline approaches such as ETL/ELT & stream processing
• Familiarity with technologies such as microservices, APIs, containerization (e.g., Docker, Kubernetes), and cloud environments (e.g., AWS)
• Strong interpersonal and verbal communication skills
• Ability to work effectively from your remote location using modern collaborative tools running on a company-provided MacBook Pro

Preferred Experience & Skills 

• Prior experience as a Machine Learning Engineer, Data Engineer, or Data Scientist
• Familiarity with Data Engineering, DataOps, and MLOps principles & tools
• Experience designing, training, and evaluating Machine Learning and/or AI models for a production environment
• Experience with Machine Learning & AI frameworks and libraries such as scikit-learn, HuggingFace, Tensorflow/Keras, MLlib, etc.
• Experience with data warehouses (e.g., dimensional modeling), data lakes/lakehouses, and/or feature stores
• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science (e.g., attribution, customer LTV, propensity, uplift models)
• Experience with deep learning architectures and frameworks (e.g., PyTorch, TensorFlow/Keras)
• Familiarity with Large Language Models (LLMs) and how they are applied in production
• Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information

Perks  

• Competitive Salary 
• A MacBook Pro and accompanying hardware to do great work. 
• A modern productivity toolset to get work done: Slack, Miro, Loom, Lucid, Google Docs, Atlassian and more. 

Equal Employment Opportunity 

At AbbVie, we value bringing together individuals from diverse backgrounds to develop new and innovative solutions for patients. As an equal opportunity employer, we do not discriminate on the basis of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information gender identity or expression, sexual orientation, marital status, protected veteran status, or any other legally protected characteristic.