Senior Research Statistician - Exploratory Analysis (Hybrid)
Key Skills for This Role
Full Job Posting
Preferred Qualifications
- Experience with Biomarker Research
- Other Required Skills:
- High degree of technical competence and effective communication skills, both oral and written
- Able to identify data or analytical issues, and assist with providing solutions by either applying own skills and knowledge or seeking help from others
- Able to build strong relationship with peers and cross-functional collaborators to achieve higher performance. Highly motivated to drive innovation by raising the bar and challenging the status quo
- Able to integrate high dimensional data from different sources. Familiar with overfitting and multiple testing control methods, such as cross-validation and random resampling techniques. Familiar with machine learning and predictive modeling methods.
- 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 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
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.
Qualifications
- MS (with 4+ years of experience) or PhD (with 0+ years of experience) in Statistics, Biostatistics, or a highly related field.
- Research experience in the pharma or healthcare space
- Experience with Machine Learning
- Experience with Biomarker Research
- High degree of technical competence and effective communication skills, both oral and written
- Able to identify data or analytical issues, and assist with providing solutions by either applying own skills and knowledge or seeking help from others
- Able to build strong relationship with peers and cross-functional collaborators to achieve higher performance. Highly motivated to drive innovation by raising the bar and challenging the status quo
- Able to integrate high dimensional data from different sources. Familiar with overfitting and multiple testing control methods, such as cross-validation and random resampling techniques. Familiar with machine learning and predictive modeling methods.
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