Remote Lead Data Scientist

Summary

Join Allergan Data Labs in transforming the Allergan Aesthetics beauty business at AbbVie. As a Lead Data Scientist, you will report to the Data Science Manager and collaborate with stakeholders to build data-driven products.

Requirements

• Completed BS, MS, or PhD in Computer Science, Statistics, Mathematics, Engineering, Data Science, or other quantitative field / equivalent experience
• At least 7 years of experience as a Data Scientist applying statistical methods and machine learning as an individual contributor to solve business problems
• Experience designing, training, and evaluating machine learning models that have been deployed into production at scale
• Strong programming skills in Python, an understanding of core computer science principles, and experience with Python data manipulation frameworks such as Pandas & PySpark
• Strong data manipulation skills in SQL, and good understanding of relational database design
• Broad knowledge of basic computational statistics and good understanding of theoretical fundamentals of statistics
• Thorough and broad knowledge of modeling and training techniques for machine learning & AI, as well as best practices for ensuring robustness and performance
• Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, MLlib, PyTorch, TensorFlow/Keras, etc
• Ability to apply statistical techniques for experimental design (e.g., A/B, multi-cell testing), causal inference methods, and time series analysis/forecasting
• Talent and skills for creating clear data visualizations for both technical & non-technical audiences
• Experience with data warehouses (e.g., dimensional modeling), data lakes/lakehouses, and other data architectures
• Familiarity with Data Engineering, DataOps, and MLOps principles and tools
• 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

Responsibilities

• Take ownership for achieving objectives and key results for the Data Science & Engineering Department
• Serve as a technical thought leader, driving best practices in data science while guiding and mentoring the data science team
• Partner closely with the Data Science Manager to align technical solutions with business objectives and contribute to strategic decision-making
• Opportunity to manage a small team of data scientists and analysts by setting goals, supervising work, evaluating performance, removing barriers, cultivating career development, and promoting job satisfaction
• In collaboration with Data Product Managers, work with cross-functional partners (Product Development, Marketing, Sales, Customer Success, etc.) to understand & document business requirements including objectives, estimated costs & benefits, inputs, accuracy, latency, scale, and governance constraints
• Make individual technical contributions, such as the following
• Collaborate with Data Engineers, Software Engineers, and other business partners to identify, gather, cleanse, and organize data sets needed for analyses & modeling projects
• Perform exploratory data analyses using appropriate techniques (descriptive statistics, visual analytics, clustering, transformations, etc.) to understand, distill, and communicate the information content of data sets, and to extract features that are appropriate for formulation of metrics or as inputs to data models
• Conduct inferential, predictive, prescriptive, causal, and other statistical analyses including hypothesis testing & forecasting to answer business questions, improve customer engagement, and drive business growth
• Design, train, and evaluate machine learning and AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc
• Collaborate with Machine Learning Engineers to design and implement machine learning & AI models as scalable & robust solutions that can be deployed into production at scale, and help develop monitoring metrics to detect non-stationary behavior, anomalies, and degradation in production
• Enforce our governance & development standards, including processes & frameworks for logging experiments, code & model quality standards, documentation, and source controlling artifacts
• Review technical designs, code implementations, and results for their appropriateness, effectiveness, and adherence to standards
• Clearly document & present your work and informational materials at the appropriate level of detail to business partners & leadership
• Maintain sharp professional knowledge & skills, and stay abreast of new developments in data science, machine learning, & AI
• Generate intellectual property and business ideas that could generate value for the company, such as new use cases and approaches to using data

Preferred Qualifications

• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science (e.g., attribution, customer LTV, propensity, uplift models)
• Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
• Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information
• Experience with MLFlow or other machine learning lifecycle tools
• Familiarity with cloud machine learning services such as Amazon SageMaker
• At least 1 year of experience managing one or more teams of data scientists and analysts
• MS or PhD in a relevant quantitative field
• Publications in data science, machine learning, AI, and/or other data-related topics

Benefits

• Competitive salary
• Competitive annual bonus targets
• 401k with dollar for dollar match, up to 6% of eligible earnings (base, bonus)
• RSU grants (Long Term Incentives) for approved roles
• Comprehensive medical, dental, vision and life insurance
• 17 paid holidays per year, including 3 floating holidays
• Annual Paid Time Off (PTO), with separate sick days
• 12 weeks paid Parental Leave
• Caregiver Leave
• Adoption and Surrogacy Assistance Plan
• Flexible workplace accommodations
• Attend AWS Re:Invent in person (Las Vegas) or virtually each year for approved roles
• Tuition reimbursement
• 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
• Generous discounts on SkinMedica skin care products