Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Pfizer
About the position
This secondment offers a Director-level individual contributor the opportunity to professionally grow within the US Business Aligned Data Science (BA-DS) team. This experience is well suited for a current data scientist or analytic-minded individual looking to expand their technical and methodological expertise into more applied analytic decision-making for commercial partners. The successful candidate is expected to carry out the functions of the BA-DS team, which are rooted in translating key commercial business questions into advanced analytic insights. Strong emphasis is placed on applying data science principles to the business problem, rigorously documenting the end-to-end analytic pipeline, purposely communicating with stakeholders, and delivering effective presentations appropriate to the target audience. The secondment is focused – but not limited – to high-impact workstreams that are anchored around two core capabilities of the BA-DS team – Dynamic Targeting and Dynamic Content. These workstreams provide the opportunity to leverage technical leadership in building and optimizing data models and processes, while also collaborating with Commercial and GCA stakeholders. Other projects may also be prioritized given specific needs and timelines.
Responsibilities
- Conduct thorough EDA and establish a baseline diagnostic of the Ig market landscape
- Perform focused, deep-dive analyses and/or models on key market trends (e.g. switching behavior, referral pathways, market access dynamics) to identify segment-level and new feature engineering opportunities
- Define key business rules, document data sources and inventory, and implement scalable, reusable data pipelines to support modeling and ongoing refresh cycles
- Translate brand objectives and new analysis insights into an initial Dynamic Targeting modeling framework and execution plan
- Partner with commercial stakeholders to review results, align on insights, and communicate downstream Dynamic Targeting implications
- Assess and modernize the dynamic content model and pipeline to improve delivery and downstream applications
- Refactor code and data pipelines to improve performance, scalability, and ease of future iterations
- Collaborate with digital strategy and BA-DS brand lead to ensure content logic aligns with current campaign strategy
- Ensure work is reproducible and potentially useable with other brands identified
- Create and implement monitoring and validation checks to ensure ongoing data quality and output integrity
- Document system architecture, decision logic, and operational procedures for long-term team ownership
- Perform lift and impact measurement analyses for Prevnar data science models
- Conduct validation exercises to assess the expected performance of model outcomes and decision outputs
- Develop lift and ROI measurement frameworks, KPIs, and visuals that can be reused across current and future projects to improve consistency and efficiency across the BA-DS team
Requirements
- Must have a bachelor's degree with at least 8 + years of experience.
- 8+ years of experience in advanced analytics (data science preferred), with at least 2 years operating at a senior or director level as an individual contributor.
- Ability to independently scope, plan, and execute complex analytical projects with minimal supervision.
- Ability to partner with key stakeholders, provide progress updates, align on timelines and deliverables, and ensure a continuous feedback dialogue throughout the project.
- Strong written and verbal communication skills, with the ability to present technical findings to senior business leaders.
- Advanced proficiency in Python or R and related libraries for data science and machine learning (e.g. Pandas, Scikit-learn, Tidyverse, Caret).
- Strong SQL skills with experience querying large-scale enterprise data warehouses (e.g., Snowflake, Redshift, BigQuery, Databricks).
- Experience using Jupyter notebooks for exploratory data analysis, data validation, and iterative analytical investigation.
- Familiarity with enterprise data science platforms (e.g. Dataiku DSS, Azure ML) and working within cloud-based data science environments.
- Proficiency in data visualization tools and libraries (e.g. Matplotlib, Seaborn, Plotly, Streamlit) for communicating analytical insights.
- Hands-on experience with Git/GitHub including branching strategies, pull requests, and code review practices.
- Knowledge of commercial pharmaceutical data assets including real-world patient claims, prescriber Rx, market access, and specialty pharma data.
- Experience refactoring legacy codebases and bringing research-quality code to production-grade standards.
- Demonstrated experience building, validating, and deploying supervised and unsupervised machine learning models in a business context.
- Strong understanding of statistical principles in relation to building and validating data science models (e.g. outliers, distribution cu
Source: Pfizer careers