Senior Data Scientist
About The Position
AbbVie is seeking an experienced Senior Data Scientist to join its Analytics & AI organization and advance Copay Intelligence capabilities. The role develops AI and machine learning solutions to identify reimbursement and affordability program dynamics, improve patient access insights, and support proactive decision-making across patient support programs. It uses healthcare, claims, enrollment, and patient-level data to identify opportunities, uncover patterns, and generate actionable recommendations. The Senior Data Scientist leads the design, development, deployment, and continuous improvement of advanced analytics and AI solutions for strategic initiatives in predictive intelligence, healthcare affordability, patient access, and machine learning. The role works across the full lifecycle of AI and machine learning product development, collaborating with data engineering, business stakeholders, technology teams, and analytics partners to turn large-scale healthcare data into scalable solutions with measurable business impact.
Requirements
- Bachelor's degree in Statistics, Computer Science, Data Science, Mathematics, Engineering, Economics, Operations Research, or a related quantitative discipline.
- Six or more years of experience developing and deploying machine learning, AI, or advanced analytics solutions in production environments.
- Demonstrated experience delivering production data science solutions and evaluating performance against business outcomes.
- Strong proficiency in Python and SQL.
- Hands-on experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
- Experience building predictive, classification, forecasting, recommendation, or optimization models.
- Strong knowledge of statistics, experimentation, predictive modeling, and model evaluation techniques.
- Experience working with large-scale data platforms and cloud-based environments.
- Familiarity with MLOps, model deployment, model monitoring, and production AI practices.
- Experience using modern software development and version control practices.
- Strong analytical and problem-solving capabilities.
- Excellent communication and stakeholder management skills.
- Ability to translate business challenges into data-driven solutions.
- Ability to work independently and collaborate effectively across teams.
- Strong organizational and project management skills.
Nice To Haves
- Advanced degree (Master's or PhD); preferred but not required.
- Experience with healthcare, pharmaceutical, claims, payer, patient support, or market access data.
- Experience developing AI-powered decision-support systems.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with Snowflake, Databricks, Spark, or other large-scale data environments.
- Experience deploying Generative AI or Large Language Model solutions.
- Experience in regulated industries where data quality, governance, and validation are critical.
- Experience rapid-prototyping or evaluating generative-AI solutions.
- Experience with experimentation or causal measurement methods.
Responsibilities
- Develop advanced machine learning and AI models for healthcare affordability and copay intelligence initiatives.
- Build predictive models that identify patterns, risks, and opportunities in healthcare, claims, enrollment, and patient-level data.
- Design scoring frameworks, recommendation engines, and decision-support systems to enable proactive business actions.
- Evaluate and implement machine learning, deep learning, ensemble methods, graph analytics, and Generative AI approaches.
- Improve model performance through experimentation, feedback loops, and performance monitoring.
- Analyze complex healthcare datasets and perform feature engineering, predictive modeling, forecasting, segmentation, optimization, and statistical analyses.
- Assess model performance using statistical and business outcome metrics.
- Partner with business stakeholders to frame ambiguous questions as analytical problems, hypotheses, and success measures.
- Translate analytical findings into clear recommendations for technical and non-technical stakeholders.
- Develop methodologies to measure business impact and return on investment for analytical solutions.
- Partner with data engineering and technology teams to deploy machine learning solutions into production.
- Apply practices for reproducibility, code quality, documentation, validation, and responsible AI throughout model development and deployment.
- Design scalable, maintainable machine learning workflows and pipelines.
- Implement model monitoring, automated retraining, model governance, and performance management processes.
- Support enterprise AI products that can scale across business functions and ensure solutions meet organizational quality, privacy, security, and compliance standards.
- Identify opportunities for AI and advanced analytics to drive measurable value, and serve as a subject matter expert in data science, machine learning, and predictive analytics.
- Present findings, recommendations, and technical concepts to senior leadership and cross-functional teams, clearly communicating trade-offs and limitations.
- Mentor junior data scientists and contribute to the analytics community.
- Stay current on emerging AI, machine learning, and healthcare analytics technologies and trends.
Benefits
- Paid time off, including vacation, holidays, and sick time.
- Medical, dental, and vision insurance for eligible employees.
- 401(k) for eligible employees.
- Eligibility to participate in long-term incentive programs.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
