Data Scientist II

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.

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.

Qualifications :

Required Qualifications:

• Bachelors degree - preferably in Computer Science or Data Science - and at least 5 years of relevant experience OR Masters 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.

Preferred Qualifications:

• Knowledge of popular libraries such as Pandas NumPy Scikit-learn TensorFlow and PyTorch.
• Experience with Tableau and Power BI visualization 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 short-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 Companys sole and absolute discretion unless and until paid and may be  modified at the Companys 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  & Puerto Rico applicants seeking a reasonable accommodation click here to learn more:

Work :

No

Employment Type :

Full-time