Location: Onsite - Princeton, NJOverview of Role:The Senior Manager, Data Scientist role is part of the Commercial Operations team supporting the U.S. Business Unit-which includes, but is not limited to, Sales, Marketing, and Market Access. This team enables key business functions and strategic initiatives through data integration, reporting, and advanced analytics.
This position provides a unique opportunity for broad exposure across the U.S. commercial organization by contributing to the development of advanced analytics solutions. A core focus of the role will involve executing analytics on a variety of data sources-including patient claims, EDI shipment data, and unstructured datasets-as well as supporting future machine learning initiatives.
Role and Responsibilities:- Design and implement advanced analytical models (e.g., predictive modeling, segmentation, optimization, machine learning) using diverse datasets such as claims, outlet-level sales data, EDI, hub data, real-world data (RWD), specialty pharmacy/distributor data, EMR, and internal commercial datasets.
- Develop and validate algorithms to identify patient journeys, adherence patterns, and treatment pathways.
- Contribute to an evolving data science practice, including problem framing, data exploration and preparation, data integration, machine learning model development, and production.
- Create scoring frameworks (e.g., HCP opportunity models, payer access risk scores, patient conversion likelihood).
- Build statistical and machine learning models for both proof-of-concept and production environments using Python, R, and SQL.
- Partner with stakeholders across the U.S. commercial teams to translate business needs into data-driven and machine learning solutions.
- Communicate advantages, limitations, and implications of analytical approaches to non-technical audiences.
- Share technical insights and solutions through design reviews, pair programming, code/model reviews, and team knowledge-sharing sessions.
- Conduct exploratory analysis of new datasets, generate descriptive statistics, identify trends and insights, and propose data engineering opportunities for integration.
- Utilize one or more commercial or open-source analysis platforms daily (e.g., Jupyter Notebook, RStudio, Posit, Microsoft Azure, Neo4j).
- All other duties as assigned.
Experience, Education and Specialized Knowledge and Skills:- Experience in biotech, pharmaceuticals, or life sciences required
- Bachelor's degree in Data Science, Computer Science, Mathematics, or a related field required; advanced degree preferred.
- Minimum 5+ years of relevant experience with a bachelor's degree, or 3+ years with a master's degree, contributing to or executing data science projects. Industry experience in customer behavior prediction, customer journey analytics, marketing analytics, or social network analysis is preferred.
- Demonstrated proficiency in Python and/or R.
- Strong SQL skills with hands-on experience working with relational databases; experience with graph databases and Cypher is a plus.
- Familiarity with machine learning and statistical modeling techniques
- Strong communication and cross-functional collaboration skills
- Experience developing solutions using text analytics, customer journey analytics, marketing analytics, or recommendation engines.
- Proven ability to explain statistical and machine learning concepts to business stakeholders in clear and meaningful terms.
- Knowledge of statistical and data mining techniques such as hierarchical clustering, network analysis, regression, random forests, text mining, NLP, and data visualization.
This is the pay for this position
Pay Transparency
$160,000-$180,000 USD
Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include bonus, stock, benefits and/or other applicable variable compensation.
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