Associate Director, Commercial Data Science at Cytokinetics in Radnor, PA

Job description

Cytokinetics is a specialty cardiovascular biopharmaceutical company, building on its over 25 years of pioneering scientific innovations in muscle biology, and advancing a pipeline of potential new medicines for patients suffering from diseases of cardiac muscle dysfunction. As a key member of the Business Insights and Analytics team within the commercial domain of Cytokinetics, you will apply advanced analytics, machine learning, and AI to a broad array of data sources (claims, payer, sales, SP, ex-factory, channel, digital engagement, field, patient services, etc.) to drive business decision making across marketing, sales, patient operations, and market access. Owning the commercial data science methodology and roadmap, you will partner directly with Marketing, Value and Access, Sales, Operations, IT, and Finance leaders, with a clear line of sight from your models to business and patient impact. Your work will include building and deploying ML models and other AI capabilities across commercial use cases, designing measurement studies and experiments to optimize spend, driving in-line and pre-launch market and performance insights, delivering scalable, automated insights and analytics across BI and other platforms, and collaborating with internal stakeholders to shape commercial and advanced analytics strategy. As Cytokinetics commercial is in a phase of rapid growth, you will also be essential to support strategic decision making by uncovering business performance and insights that require deep and complex exploration of large data sets. This role blends technical depth, business acumen, and the ability to communicate insights effectively to executive and other non-technical audiences. The ideal candidate has a strong background in building scalable advanced analytics and predictive models, analyzing market trends and the competitive landscape, and partnering closely across the insights, data, and IT teams to uncover opportunities that create further value for patients. Responsibilities: Business Partnership & Problem Framing Act as a strategic partner to internal stakeholders by framing key business questions as tractable analytical problems that align to brand objectives. Partner cross-functionally to understand commercial strategies, prioritize use cases by expected business impact, and translate business needs into data science and AI solutions. Business, Market & Performance Insights Analyze business performance and market data from industry (e.g., anonymized longitudinal patient claims data, remittance claims, payer policy and lives data) and internal (e.g., field and digital customer interactions, sales data) sources to answer key business questions, identify trends, and proactively uncover optimization opportunities across in-line and pre-launch brands. Machine Learning, Measurement & Advanced Analytics Design, build, and deploy supervised and unsupervised ML models, including classification, regression, time-series, clustering, and causal/uplift modeling, taking solutions from feature engineering through validation, production deployment, and monitoring. Design and analyze measurement studies and experiments (e.g., A/B and holdout tests, incrementality) and apply ML and other predictive capabilities to marketing mix models to improve investment decisions and optimize spend across channels. Patient & Customer Analytics Conduct patient analytics using real-world data (RWD) sources such as anonymized longitudinal patient claims and lab/diagnostic testing data to characterize patient populations, treatment patterns, and unmet needs. Analyze patient journeys (e.g., diagnosis, referral, treatment initiation, and persistence) to identify trends and to predict and size performance opportunities and gaps. Develop recommender systems to target commercial action based on patient and HCP data. Develop predictive models to anticipate customer needs, improve customer experience, and optimize promotional efforts across field and