Associate Director, Business Insights & Analytics Data Scientist

Cytokinetics•Radnor, PA

1d•$220,500 - $257,250•Hybrid

About The Position

Cytokinetics is seeking a commercial data scientist to join its Business Insights and Analytics team. The role applies advanced analytics, machine learning, and AI to commercial data—including claims, payer, sales, specialty pharmacy, ex-factory, channel, digital engagement, field, and patient services data—to support decisions across marketing, sales, patient operations, and market access. The position owns the commercial data science methodology and roadmap and partners with leaders across Marketing, Value and Access, Sales, Operations, IT, and Finance. The role builds and deploys machine-learning models and other AI capabilities, designs measurement studies and experiments to optimize spending, delivers in-line and pre-launch market and performance insights, and develops scalable, automated analytics. It supports strategic decisions through complex exploration of large datasets and requires technical depth, business acumen, and effective communication with executive and non-technical audiences. The expected salary range for the primary location of Radnor, Pennsylvania, is $220,500–$257,250. Base pay may vary with geographic region, job-related knowledge, skills, experience, and other factors. The total compensation package includes equity in the form of stock options and/or restricted stock units, comprehensive insurance coverage, 401(k) retirement benefits with employer match, learning and wellness stipends, and paid time off; benefits may vary by jurisdiction and are subject to change.

Requirements

Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field, with at least 7 years of hands-on experience in data science, machine learning, advanced analytics, or related disciplines; alternatively, a bachelor’s degree in one of these fields with at least 10 years of similar experience.
Deep expertise in statistical and predictive modeling, machine learning, causal inference, experimentation, and measurement.
Expertise in promotional effectiveness, marketing mix modeling (MMM), multi-touch attribution (MTA), digital and web analytics, A/B testing, and media performance analysis.
Strong proficiency in Python, SQL, and statistical analysis.
Experience working in cloud-based notebook environments, such as Jupyter, on large-scale healthcare datasets.
Track record of deploying and monitoring machine-learning models in production for commercial use cases.
Working proficiency with BI tools such as Tableau or Power BI to create actionable dashboards for executive and business audiences.
Broad knowledge of pharmaceutical commercial functions, such as marketing, sales, market access, and patient services, and the data and technology environments supporting them.
Excellent communication and interpersonal skills, including the ability to influence cross-functional and executive stakeholders through data-driven storytelling.
Demonstrated success driving cross-functional initiatives from ideation through production.

Nice To Haves

Experience with MLOps tools and practices, such as CI/CD, experiment tracking, and model monitoring, to maintain model stability and scalability.
Experience with Generative AI, large language models, and agentic models.

Responsibilities

Frame business questions as tractable analytical problems aligned with brand objectives.
Partner cross-functionally to understand commercial strategies, prioritize use cases by expected impact, and translate business needs into data science and AI solutions.
Analyze external market and internal commercial data to answer business questions, identify trends, and uncover optimization opportunities for in-line and pre-launch brands.
Design, build, validate, deploy, and monitor supervised and unsupervised machine-learning models, including classification, regression, time-series, clustering, and causal or uplift modeling.
Design and analyze measurement studies and experiments, including A/B and holdout tests and incrementality analyses.
Apply machine learning and predictive capabilities to marketing mix models to improve investment decisions and optimize channel spending.
Use real-world data, including anonymized longitudinal claims and lab or diagnostic data, to characterize patient populations, treatment patterns, and unmet needs.
Analyze patient journeys, including diagnosis, referral, treatment initiation, and persistence, to identify trends and estimate performance opportunities and gaps.
Develop recommender systems using patient and HCP data to target commercial actions.
Develop predictive models to anticipate customer needs, improve customer experience, and optimize promotional efforts across field and digital channels, including segmentation, targeting, and next-best-action.
Partner with BI and IT teams to design dashboards and self-service tools, standardize metrics, consolidate reports, and automate recurring reporting and analytics.
Work with data strategy and governance partners to promote consistent metric definitions and use of approved data foundations, and steward standardization and governance best practices.
Translate analytical results into compelling narratives and recommendations that earn stakeholder buy-in and drive action.
Communicate technical concepts effectively to marketing, sales, market access, finance, legal/compliance, and other stakeholders.