This role pays more than
64%
of similar roles. Most pay
$137,743–$202,289
— the shaded band above.
At the midpoint, this role pays about
$180k
versus about
$170k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
297 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 271 roles with salary data.
Principal Data Scientist serves as the technical lead within the Analytics and Performance Excellence team, overseeing a portfolio of advanced analytics products for personalized commercial engagement and performance optimization. This role is responsible for the end-to-end technical strategy, model design, development, and validation of AI/ML and statistical solutions to drive data-driven decision making in a pharmaceutical context. The successful candidate will build predictive and inferential models using multi-source data to analyze customer behavior and engagement patterns. Key technologies include Python, R, SQL, pandas, NumPy, scikit-learn, PySpark, and deep learning frameworks like PyTorch or TensorFlow. Candidates must possess expertise in causal inference, experimentation, and omnichannel analytics. The role involves productionalizing models in cloud environments while translating complex technical findings into actionable insights for non-technical stakeholders to improve commercial effectiveness and measurement.
What does a Data Scientist earn?
Median $162025 from 276 postings across 61 companies.
Lead the end-to-end technical strategy, design, and validation of AI/ML and statistical models for commercial engagement.
Translate business requirements into scalable technical solutions including feature engineering, model development, and deployment.
Develop predictive and inferential models to identify customer behavior patterns and drivers of business performance.
Design analytics approaches to evaluate cross-channel engagement patterns and temporal dynamics for coordinated strategies.
Establish measurement methodologies using experimental and observational methods to determine effectiveness and business impact.
Partner with engineering teams to productionalize, monitor, and manage the lifecycle of AI/ML solutions.
Provide technical mentorship to junior and mid-level data scientists on advanced analytics projects.
Synthesize complex technical findings into actionable insights for non-technical stakeholders and executive leadership.
What we're looking for
Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline is required.
Master’s or PhD in a quantitative field is preferred.
8+ years of experience in data science, machine learning, or advanced analytics with production-quality model delivery is required.
5+ years of experience in pharmaceutical, biotech, healthcare, or life sciences commercial analytics is preferred.
4+ years of experience in omnichannel analytics and 4+ years of experience measuring commercial effectiveness using experimental methods are required.
Proficiency in Python (including pandas, NumPy, scikit-learn, PySpark) and SQL is required.
Experience productionizing AI/ML solutions in cloud environments and hands-on experience with deep learning frameworks like PyTorch or TensorFlow are required.
Advanced ML techniques, pharmaceutical data familiarity, MLOps tools, and LLM-based workflows are preferred.
Remote (San Francisco, CA)+325 days ago
$227,850–$423,150
Machine Learning
Deep Learning
Recommender Systems
Python
TensorFlow
Keras
PyTorch
SQL
AWS
Snowflake
Spark
Google Cloud
NLP
Reinforcement Learning
Bandits
Collaborative Filtering
Information Retrieval
Big Data
Causal Inference