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About This Role
Company Description About AbbVie
AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience \- and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.
Job Description
The Principal Data Scientist will serve as a strategic leader and technical expert driving enterprise\-level transformation through the convergence of R\&D data assets and the systematic integration of advanced AI/ML capabilities into organizational workflows. This role will work with cross\-functional teams to define and architect scalable solutions that fundamentally transform how AbbVie leverages data and artificial intelligence to enhance strategic decision\-making processes across the R\&D pipeline and clinical development continuum.
- Acting as a key member of the R\&D Convergence Core team, this position will identify systemic gaps in data convergence and workflow integration, then lead collaborative efforts to design and implement enterprise\-wide solutions that create sustainable organizational capabilities rather than isolated point solutions. The Senior Principal Data Scientist will work at the intersection of scientific strategy, advanced analytics, and organizational transformation to establish integrated frameworks that enable scalable AI/ML adoption across therapeutic areas and functional domains.
- This role requires demonstrated expertise in translating cutting\-edge AI technologies \-including advanced machine learning, deep learning, and generative AI \- into production\-ready workflow integrations that drive measurable improvements in R\&D innovation and efficiency, clinical trial optimization, and strategic portfolio decision\-making processes. The position demands a proven track record of leading enterprise\-scale data and AI initiatives that achieve cross\-functional adoption and deliver sustained organizational impact
Responsibilities:
Enterprise Strategy \& Workflow Transformation
- Lead the identification and assessment of enterprise\-level gaps in data convergence, analytical capabilities, and AI/ML integration across R\&D workflows; design comprehensive strategies to address systemic challenges through scalable, integrated solutions
- Architect and champion collaborative cross\-functional frameworks that enable the systematic integration of advanced AI/ML capabilities into existing R\&D and clinical workflows, ensuring solutions are extensible, maintainable, and aligned with enterprise data strategies.
- Drive organizational transformation initiatives that fundamentally enhance how data and AI inform strategic decision\-making across the R\&D portfolio, therapeutic development, and clinical trial execution
- Establish and evangelize best practices, standards, and governance frameworks for enterprise\-wide AI/ML workflow integration that ensure consistency, quality, and regulatory compliance
Advanced AI/ML Solution Architecture
- Oversee the development of sophisticated, production\-grade AI/ML workflow orchestrations that integrate multiple data sources, analytical techniques, and decision support capabilities into cohesive enterprise solutions
- Lead the collaborative application of state\-of\-the\-art AI technologies including advanced machine learning, deep learning architectures, natural language processing, and generative AI to transform complex R\&D and clinical development processes
- Architect end\-to\-end analytical pipelines that seamlessly connect data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement within enterprise platforms.
- Drive innovation in workflow automation and intelligent process optimization, leveraging AI/ML to reduce cycle times, enhance quality, and improve decision accuracy across the R\&D continuum
Cross\-Functional Leadership \& Collaboration
- Serve as the technical leader for high\-impact, cross\-functional initiatives requiring advanced data convergence and AI/ML integration across multiple therapeutic areas and R\&D functions
- Partner with senior leadership across R\&D, IT, Data Science, and business functions to align workflow transformation initiatives with strategic priorities and ensure executive\-level buy\-in
- Represent R\&D in enterprise\-wide forums and decision\-making bodies related to data strategy, AI governance, and technology architecture, advocating for solutions that balance innovation with scalability and compliance
Organizational Capability Building
- Oversee development of reusable, modular AI/ML components and workflow templates that can be rapidly adapted across different therapeutic areas and functional domains
- Collaborate with Data Engineering, MLOps, and IT teams to establish robust infrastructure and platforms that support the scalable deployment and operation of integrated AI/ML workflows
- Establish metrics and monitoring frameworks to continuously assess the impact of AI/ML workflow integrations on R\&D efficiency, decision quality, and strategic outcomes
Technical Excellence \& Innovation
- Maintain deep expertise in the latest advances in artificial intelligence, machine learning, and analytical methodologies; evaluate emerging technologies for their potential to drive enterprise\-wide workflow transformation
- Ensure all AI/ML solutions adhere to regulatory requirements, data governance policies, ethical AI principles, and AbbVie quality standards
- Design solutions with security, privacy, auditability, and explainability considerations embedded from inception, particularly for regulated clinical and healthcare applications
- Translate complex technical architectures and AI/ML methodologies into clear strategic narratives for diverse stakeholders, from technical teams to executive leadership
Qualifications
- PhD in Computer Science, Statistics, Bioinformatics, Computational Biology, Applied Mathematics, Data Science, or related quantitative field strongly preferred; Master's degree with exceptional demonstrated expertise and extensive experience considered
- 4\-5\+ years of progressive experience building, deploying, and scaling advanced AI/ML solutions in enterprise environments, with demonstrated leadership of large\-scale, cross\-functional data and analytics initiatives.
- Proven track record of leading enterprise\-wide workflow projects that resulted in measurable organizational impact and sustainable capability development
- Minimum 5\+ years of experience working in highly matrixed, complex organizational environments (Preferred experience in Consulting across pharmaceutical, biotech, healthcare, or similarly regulated industries strongly preferred)
Technical Expertise
- Expert\-level proficiency in advanced machine learning and artificial intelligence, including deep learning, neural network architectures, ensemble methods, transfer learning, and generative AI technologies
- Demonstrated mastery of ML/AI frameworks and platforms (e.g., TensorFlow, PyTorch, Scikit\-learn, Hugging Face) and their application to complex, real\-world problems
- Advanced programming capabilities in Python and R, with strong software engineering principles; experience with production code development, version control, CI/CD pipelines, and testing frameworks
- Deep understanding of MLOps principles, model lifecycle management, workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow), and enterprise deployment architectures
- Experience with cloud computing platforms (AWS, Azure, others) and distributed computing frameworks for large\-scale data processing and model training
- Strong expertise in data architecture, data integration patterns, and modern data platforms supporting enterprise analytics
Strategic \& Leadership Capabilities
- Demonstrated success leading enterprise\-scale initiatives that transform organizational workflows and decision\-making processes through data and AI integration
- Proven ability to influence senior leadership, build cross\-functional coalitions, and drive adoption of complex technical solutions across large, matrixed organizations
- Strong change management acumen and experience driving organizational transformation in regulated environments
- Track record of successful collaboration with multidisciplinary teams including data scientists, software engineers, clinicians, scientists, and business stakeholders
Domain Knowledge
- Experience in pharmaceutical R\&D, clinical development, or healthcare analytics strongly preferred
- Understanding of regulatory requirements, clinical trial design, drug development lifecycle, and healthcare data governance preferred
- Working knowledge of healthcare data standards (e.g., CDISC, OMOP) and FAIR data frameworks preferred
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
Salary Context
This $141K-$268K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 245 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At AbbVie, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $141K to $268K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
AbbVie AI Hiring
AbbVie has 8 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Research Scientist. Positions span North Chicago, IL, US, Worcester, MA, US, Florham Park, NJ, US. Compensation range: $125K - $305K.
Location Context
AI roles in Chicago pay a median of $200,100 across 329 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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