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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 Analytics and Performance Excellence (APEX) function supports AbbVie's US Commercial organization and is comprised of highly regarded researchers, analysts, data scientists and strategists who are committed to being best\-in\-class within the biopharmaceutical industry. We serve as a strategic in\-house counsel, ensuring that all decisions leverage the key insights that we develop. We continue to build new capabilities and skill sets, encouraging our team members to pull up a chair, be themselves, be creative, speak their minds, and do good work. We are a passionate, diverse, flexible, and inclusive organization with a culture that supports the best ideas, wherever they originate. We are smart, fun, quirky, and innovative – and we'd love for you to join us.
The Principal Data Scientist is the technical lead for a portfolio of advanced analytics products that support personalized commercial engagement and performance optimization. Operating within the APEX Enterprise Advanced Analytics and Innovation team, this role is accountable for the end\-to\-end technical strategy, model design, methodology, development, and validation of AI/ML and statistical solutions. These solutions enable data\-driven decision making across customer engagement, targeting, optimization, measurement, and ongoing performance improvement. The role reports to the Associate Director, Data Science and serves as the primary technical partner to the analytics product owner, translating business outcomes and product requirements into rigorous, scalable, and actionable analytics solutions. The position requires deep expertise in applied machine learning, statistical modeling, experimentation, and omni\-channel analytics within a pharmaceutical commercial context.
Key Responsibilities
- Lead a portfolio of advanced analytics capabilities across customer understanding, engagement planning, decision support, and measurement.
- Translate product requirements and business goals into scalable technical solutions, including model design, feature engineering, validation, and deployment.
- Lead the design and development of predictive and inferential models that generate actionable insights on customer behavior, engagement opportunities, and drivers of business performance using complex, multi\-source data.
- Design analytics approaches that evaluate cross\-channel engagement patterns, interaction effects, and temporal dynamics to inform coordinated customer strategies.
- Own measurement methodologies to measure effectiveness, incrementality, and business impact using experimental and observational methods.
- Partner with BTS, Digital Lab, engineering, and platform teams to build scalable, production\-ready analytics and AI/ML solutions.
- Establish best practices in model development, including code quality, documentation, reproducibility, peer review, version control, and methodological rigor.
- Synthesize complex technical findings into clear, actionable insights and recommendations for non\-technical stakeholders
- Partner with engineering and platform teams to productionalize AI/ML solutions, including deployment, monitoring, and lifecycle management.
- Stay current with advances in applied machine learning, causal inference, and pharmaceutical analytics, proactively identifying and piloting emerging methods that could enhance the existing and new capabilities.
Supervisory / Management Responsibilities
This role does not carry formal direct report responsibilities at this level but is expected to provide technical mentorship and hands\-on guidance to junior and mid\-level data scientists supporting the advanced analytics team. The Principal Data Scientist may lead technical workstreams with external analytics vendors and partners, including oversight of deliverable quality and methodology validation.
Key Competencies
- Deep expertise in machine learning, statistical modeling, and causal inference, with a track record of delivering production\-quality analytics solutions.
- Strong ability to lead technical execution with Product Owners and cross\-functional teams.
- Ability to translate ambiguous business problems into well\-scoped technical solutions with clear methods and success metrics.
- Hands\-on knowledge of the full data science lifecycle, from exploration and feature engineering to deployment and monitoring.
- Strong software engineering discipline, including testing, documentation, reproducibility, and version control.
- Deep familiarity with pharmaceutical data ecosystems, including HCP, patient, and engagement data.
- Strong communication skills with the ability to explain complex technical concepts to non\-technical audiences.
- Strong understanding of AI engineering principles, including deployment, monitoring, and enterprise integration.
- Ability to work across data science, engineering, and platform teams to operationalize solutions at scale.
Qualifications
- Bachelor’s Degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline required; Master’s or PhD in a quantitative field strongly preferred.
- 8\+ years of experience in data science, machine learning, or advanced analytics, with delivery of production\-quality models and solutions.
- 5\+ years of experience in pharmaceutical, biotech, healthcare, or life sciences commercial analytics is highly preferred.
- 4\+ years of experience in omnichannel analytics, including journey analysis, touchpoint measurement, engagement optimization, or next\-best\-action modeling.
- 4\+ years of experience measuring commercial effectiveness and business impact using methods such as promotional response analysis, closed\-loop measurement, A/B testing, and quasi\-experimental techniques.
- Strong proficiency in Python and/or R and strong SQL skills; hands\-on Python experience with pandas, NumPy, scikit\-learn, and PySpark for data transformation, feature engineering, model development, and scalable workflows.
- Hands\-on experience with deep learning frameworks such as PyTorch or TensorFlow for sequential, temporal, or recommendation use cases.
- Experience working with product owners, business stakeholders, and cross\-functional teams to translate requirements into technical solutions.
- Experience productionizing AI/ML solutions in cloud environments, including deployment, monitoring, and lifecycle management.
Preferred Experience
- Experience with advanced ML and optimization methods, including reinforcement learning, multi\-armed bandits, transformers, and other deep learning approaches for sequential, recommendation, or next\-best\-action use cases.
- Familiarity with pharmaceutical data sources such as IQVIA, Symphony Health, APLD, or similar Rx, claims, and engagement datasets.
- Experience working in agile product development environments.
- Exposure to cloud ML platforms and MLOps tools such as Azure ML, Databricks, and MLflow.
- Experience presenting analytical methods and findings to senior or executive audiences.
- Exposure to modern AI engineering approaches, including LLM\-based workflows, orchestration frameworks, or agentic AI patterns.
- Experience integrating AI/ML solutions using APIs, pipelines, or orchestration 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 this posting 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 and until 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 $124K-$236K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 6% below the category median. Disclosed range: $124K to $236K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
AbbVie AI Hiring
AbbVie has 4 open AI roles right now. They're hiring across Data Scientist, Research Scientist, AI/ML Engineer. Positions span Florham Park, NJ, US, North Chicago, IL, US, Worcester, MA, US. Compensation range: $162K - $236K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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