Senior Data Scientist

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.

Come to work each day with an inclusive and collaborative business technology team. As a Senior Data Scientist in AbbVie Business Technology Solutions (BTS), you will have opportunities to contribute to the digital transformation of a leading biopharma company, helping to create solutions that impact patients and their communities for the better.

A successful candidate will bring advanced analytics knowledge of best-in-class data science methodologies, deep understanding of AI/ML space, proven history of solving business problems using data science and ability to work with cross-functional teams to deliver and execute complex analytics problems.

Responsibilities

Discern whether a business problem needs a classical Machine Learning approach, a Generative AI, or Agentic solutions.; Apply structured frameworks like CRISP-DM or other approach to deliver value in a consistent, process driven way.; Develop processes and tools to monitor and analyze model performance and data accuracy and lead the advanced analytics framework through AI/ML.; Work with complex data sets (unstructured and structured data) to solve business use cases by establishing robust measurement frameworks and deliver business value with data-driven insights to senior leaders.; Perform data discovery and analysis to discover trends and patterns, meaningful insights and apply strong knowledge of data science for making informed decisions.; Design and develop data visualizations to effectively communicate insights to both technical and non-technical audiences.; Establish and follow data science’s best practices including peer review, code review, documentation, coding standards, data quality and ensure reproducibility and compliance.; Serve as internal Data Science SME and on advanced analytics platform like Dataiku and drive the self-service citizen data science COE with office hours training and establish data governance around the curated datasets and metrics, and best practices on taxonomy.; Initiate and drive projects with minimal oversight and guidance.; Tools and skills you will use in this role:; Data Prep and Data science platforms like Databricks or SageMaker; Data Visualization Tools like Power BI, Tableau, and ThoughtSpot; Advanced SQL, Python, Spark, and a broad array of ML frameworks; Lakehouse data storage patterns like Iceberg and Delta; JIRA, CI/CD and Agile project methodology; Predictive and prescriptive models, other statistical models, and knowledge in data science in general; Cloud environment (AWS) with large datasets and with streaming data architectures.; Large Language Models and Generative AI; ML frameworks (Scikit-Learn, NumPy, SciPy, Pandas, XGBoost, TensorFlow, PyTorch, MXNet, LLM); Classical ML validation as well as Agentic and Generative AI evaluation techniques.; ML Registries like MLFlow

Requirements

Bachelor's Degree with 7 years of experience, Master's Degree with 6 years of experience, PhD with 2 years of experience.; Respective years of working experience in data science framework and knowledge of using all the statistical or AI/ML learning models to address business use cases with extensive experience with statistical/analytical methodologies and machine learning algorithms (e.g. classification, regression, clustering, forecasting, feature selection/engineering, deep learning, time-series analysis, hypothesis testing, anomaly detection).; Respective years of experience in advanced SQL, Python, Spark, and a broad array of ML frameworks.; Respective years of experience with advanced analytics and data science platforms like SageMaker.; Respective years of experience with data lakes like Iceberg or Delta.; Respective years of experience in developing and deploying solutions in a Cloud environment (AWS, Azure) with large datasets and with streaming data architectures.; Respective years of experience in using MLflow for model registry, experiment tracking, and model validation.; Minimum 2 years of experience with GenAI application development, LLM prompt engineering, LLM-based solution architecture like RAG, developing Chatbots and AI agents using both unstructured and structured data.; Experience in data mining, data discovery, building curated data sets, insight generation, advanced analytics, and data science.; Self-starter, comfortable with ambiguity, experience initiating and driving projects with minimal oversight and guidance.

Nice to have

Experience with Pharma data, including insights into commercial sales and field operations is a plus.; Respective years of experience with Data Visualization tools like Power BI and Tableau or ThoughtSpot.; Consulting experience in Data science and Advanced Analytics domain.; 5+ years of working experience in data science framework and knowledge of using all the statistical or AI/ML learning models to address business use cases with extensive experience with statistical/analytical methodologies and machine learning algorithms (e.g. classification, regression, clustering, forecasting, feature selection/engineering, deep learning, time-series analysis, hypothesis testing, anomaly detection).; 5+ years of experience in advanced SQL, Python, Spark, and a broad array of ML frameworks.; 3+ years of experience with advanced analytics and data science platforms like Dataiku.; 3+ years of experience with Data Visualization tools like Power BI and Tableau or ThoughtSpot.; 3+ years of experience with data lakes like Iceberg or Delta.; 3+ years of experience in developing and deploying solutions in a Cloud environment (AWS, Azure) with large datasets and with streaming data architectures.; 3+ years of experience in using MLflow for model registry, experiment tracking, and model validation.