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Advanced Analytics Manager
AstraZeneca
Mississauga, CAN
Full-time
Mid
Hybrid
CAD 114333.6-150062.85 yearly / year
Discovered 3 days ago
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Responsibilities
- Lead the end-to-end development and deployment of machine learning and statistical models for both business impact measurement and the generation of timely, actionable recommendations.
- Embed model outputs into enterprise data pipelines and reporting to support ongoing decision-making.
- Partner with stakeholders across local and global commercial teams to understand business objectives and identify strategic opportunities to optimize data processes and apply advanced analytics and data science solutions.
- Perform data mining, wrangling and feature engineering to support model and report development.
- Communicate results to technical and non-technical stakeholders at the appropriate level of detail.
- Consult and provide expertise on the design, execution, and evaluation of A/B testing to assess marketing performance and enable the optimization of our go-to-market tactics.
- Partner with business unit analytics managers to enhance segmentation and targeting processes.
Qualifications
- Strong analytical and conceptual thinking skills, with the ability to manipulate data and derive meaningful insights.
- Undergraduate degree in a technical field (Statistics, Economics, Mathematics, Computer Science, etc.) or equivalent experience in a related discipline.
- Excellent problem-solving skills and a commitment to continuous learning.
- Demonstrable proficiency in SQL and Python for data processing and model development (e.g., Redshift SQL, pandas, PySpark).
- Comfortable with designing and implementing statistical and machine learning models (e.g., advanced regression techniques, hypothesis testing, time series analysis, experimental design, causal inference, clustering). Specific experience in market mix modeling, recommendation engines (Next Best Action) and Bayesian probabilistic programming is preferred.
- Demonstrable understanding of model and analytical pipeline deployment using CI/CD best practices. Experience with Git and cloud technologies for deployment (e.g., AWS, Azure, Snowflake, Databricks) is considered an asset.
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