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Lead Machine Learning EngineerAllergan Aesthetics • United States
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Lead Machine Learning Engineer

Allergan Aesthetics
  • US
    United States
  • US
    United States
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Über

Job Description
Job Description Company Description
About AbbVie At Allergan Aesthetics, an AbbVie company, we develop, manufacture, and market a portfolio of leading aesthetics brands and products. Our aesthetics portfolio includes facial injectables, body contouring, plastics, skin care, and more. Our goal is to consistently provide our customers with innovation, education, exceptional service, and a commitment to excellence, all with a personal touch. For more information, visit
https://global.allerganaesthetics.com/.
Follow Allergan Aesthetics on LinkedIn. Job Description
Responsibilities: Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning products Take ownership of objectives and key results for your workstream, and own technical solutions in partnership with your manager Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale Champion code quality, reusability, scalability, maintainability, and security, and provide input into strategic architecture decisions Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices Integrate Machine Learning and AI systems with production applications Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community Qualifications
Required Experience & Skills: Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field 7+ years of experience as an engineer specialized building Machine Learning systems 2+ years of technical leadership delivering machine learning solutions in partnership with engineers, scientists, and business stakeholders Strong programming skills in Python and understanding of core computer science principles Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc. Ability to 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. Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection, etc. Experience with building batch and streaming pipelines using complex SQL, PySpark, Pandas, and similar frameworks Experience with data warehouses (e.g., dimensional modeling), data lakes/Lakehouses, and other data architectures Experience orchestrating complex workflows and data pipelines using Airflow or similar tools Ability to load test deployed models at scale to identify performance bottlenecks Experience with Git, CI/CD pipelines, Docker, Kubernetes Experience with architecting solutions on AWS or equivalent public cloud platforms Experience with developing data APIs, Microservices and event driven systems to integrate ML systems Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production Experience in assessing and implementing new data tools to enhance the machine learning stack Strong interpersonal and verbal communication skills Technical leadership experience and the ability to mentor and guide others Preferred Experience & Skills: Knowledge of data mesh concepts Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, DataCataloging tools, Data Observability tools and Data Governance tools
Additional Information
Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: ​ The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of thisposting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location,and we may ultimately pay more or less than the posted range. This range may be modified in the future. ​
We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick),
medical/dental/vision
insurance and 401(k) to eligible employees.​
This job is eligible to participate in our long-term incentive programs. ​
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus,
commission,incentive,
benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless anduntil paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law. AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community.
Equal Opportunity
Employer/Veterans/Disabled. US & Puerto Rico only - to learn more, visit
https://www.abbvie.com/join -us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: https://www.abbvie.com/join -us/reasonable- accommodations.html
  • United States

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