Data Scientist II

AbbVie•North Chicago, IL

1d•$78,500 - $141,000

About The Position

The Data Scientist II is a technical expert that will help address critical business needs by focusing on exploratory data analysis, feature engineering, building and validating predictive/statistical models, and translating model outputs into actionable financial insights (e.g., risk scoring, fraud detection, algorithmic trading). Additionally, the role involves developing and implementing solutions using generative AI and agentic AI technologies to automate data analysis, enhance predictive modeling, and support intelligent decision-making processes within financial contexts. A continuous drive to improve existing methods and processes is essential. This position is expected to demonstrate proficiency in a broad range of techniques and methods for data and health sciences, including data warehousing, statistics, and/or machine learning.

Requirements

Bachelor’s degree - preferably in Computer Science or Data Science - and at least 5 years of relevant experience OR Master’s degree in relevant field and at least 4 years of experience.
Programming skills in Python, R, or SQL.
Hands-on experience in data science and data analytics.
Demonstrated experience in prompt engineering, fine-tuning, and evaluating generative or agentic models in a corporate context.
Ability to multitask and work within timelines.
Demonstrated ability to learn, understand and master new technologies.

Nice To Haves

Knowledge of popular libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
Experience with Tableau and Power BI visualization tools.

Responsibilities

Analyze complex datasets to generate insights, make predictions, and solve business problems.
Collaborate with engineering teams and business analysts to implement solutions and communicate findings.
Lead solutioning for finance use cases across predictive analytics, classical ML, and gen AI.
Translate business problems into analytic and AI approaches.
Own experimentation, model evaluation, and feature design.
Work with junior data scientists to raise the technical quality of outputs.
Support prompt engineering, RAG design, evaluation frameworks, and responsible AI practices.
Partner with architects and ML engineers to ensure solutions are deployable and useful.
Understand and adhere to corporate standards regarding applicable Corporate and Divisional Policies, including code of conduct, safety, GxP compliance, and data security.