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 Cosmetic, CoolSculpting, JUVÉDERM and more. The medical aesthetics business is ripe for rapid growth and disruption, and we are looking to add to our high performing team to do just that.
Our team has successfully launched a new and innovative technology platform, Allē, which serves millions of consumers, tens of thousands of aesthetics providers and thousands of colleagues throughout the US. Since its launch in November 2020, Allē has delivered curated promotions, personalized experiences and had millions of consumers use it as part of their beauty journey.
We’re looking to add to our team as we prepare to launch a new array of game-changing technologies on our successfully adopted platform. If you’re interested in working within a startup-oriented environment, while having the backing of a very large company, please read on.
As a Lead Data Scientist, you will report to the Data Science Manager and continuously collaborate with key stakeholders across the business to build data-driven products.
Responsibilities
- Take ownership for achieving objectives and key results for the Data Science & Engineering Department, allocate resources, oversee & own technical solutions, and work with Data Technical Project Managers to communicate schedules, statuses, and milestones.
- 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 collaborating 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.
Required Experience & Skills
- 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.
Preferred Experience & Skills
- 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.
Our Core Values
- Be Humble: You’re smart yet always interested in learning from others.
- Work Transparently: You always deal in an honest, direct and transparent way.
- Take Ownership: You embrace responsibility and find joy in having the answers.
- Learn More: Through blog posts, newsletters, podcasts, video tutorials and meetups you regularly self-educate and improve your skill set.
- Show Gratitude: You show appreciation and return kindness to those you work with.
Competitive annual bonus targets.
401k with dollar for dollar match, up to 6% of eligible earnings (base, bonus). Plus additional company contribution.
RSU grants (Long Term Incentives) for approved roles.
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.
We celebrate our wins with opportunities to attend Lakers, Knicks, Anaheim Ducks, Anaheim Angels and NY Rangers games.
Opportunities to attend concerts, festivals and other live entertainment events in recognition of delivering great work.
Attend AWS Re:Invent in person (Las Vegas) or virtually each year for approved roles.
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.
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