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Cytokinetics

Develops small-molecule therapies for muscle function

Associate Director - Commercial Data Science

Data ScienceConfirmed live in the last 24 hours
Full-time$220.5k - $257.3k /yrIn-person

About the Job

Requirements

  • A Master's or PhD in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field with at least seven years of hands-on experience in data science, machine learning, advanced analytics, or related disciplines; alternatively, a Bachelor's degree in those fields with at least ten years of similar hands-on experience.
  • Deep expertise in statistical and predictive modeling, machine learning, causal inference, experimentation, and measurement.
  • Expertise in promotional effectiveness, marketing mix modeling, multi-touch attribution, digital and web analytics, A/B testing, and media performance analysis.
  • Strong proficiency in Python, SQL, and statistical analysis, with experience working in cloud-based notebook environments such as Jupyter on large-scale health care datasets.
  • A track record of deploying and monitoring machine learning models in production to address commercial use cases.
  • Working proficiency with business intelligence tools such as Tableau or Power BI to translate analytical outputs into actionable dashboards for executive and business audiences.
  • Broad domain knowledge of pharmaceutical commercial functions, including marketing, sales, market access, and patient services, and the data and technology environments that support them.
  • Excellent communication and interpersonal skills, with the ability to influence cross-functional and executive stakeholders through data-driven storytelling.
  • Demonstrated success driving cross-functional initiatives from ideation through production.

Responsibilities

  • Act as a strategic partner to internal stakeholders by framing key business questions as tractable analytical problems aligned with brand objectives.
  • Partner cross-functionally to understand commercial strategies, prioritize use cases by expected business impact, and translate business needs into data science and artificial intelligence solutions.
  • Analyze industry business performance and market data, including anonymized longitudinal patient claims, remittance claims, payer policy and lives data, as well as internal field and digital customer interactions and sales data, to answer key business questions, identify trends, and proactively uncover optimization opportunities across in-line and pre-launch brands.
  • Design, build, and deploy supervised and unsupervised machine learning models, including classification, regression, time-series, clustering, and causal/uplift modeling, from feature engineering through validation, production deployment, and monitoring.
  • Design and analyze measurement studies and experiments, including A/B and holdout tests and incrementality, and apply machine learning and other predictive capabilities to marketing mix models to improve investment decisions and optimize spend across channels.
  • Conduct patient analytics using real-world data sources, including anonymized longitudinal patient claims and lab/diagnostic testing data, to characterize patient populations, treatment patterns, and unmet needs.
  • Analyze patient journeys, including diagnosis, referral, treatment initiation, and persistence, to identify trends and predict and size performance opportunities and gaps.
  • Develop recommender systems to target commercial action based on patient and health care professional data.
  • 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 business intelligence and information technology teams to design intuitive dashboards and self-service tools, standardize metrics, consolidate redundant reports, and automate recurring reporting and analytics so insights reach stakeholders at scale.
  • Partner with internal data strategy and data governance teams to ensure consistency and standardization in metric definition and use through approved data foundations, ensure metric understanding with key stakeholders, and steward best practices in standardization and governance.
  • Translate analytical results into compelling narratives and recommendations that earn stakeholder buy-in and drive action on data-driven initiatives.
  • Communicate complex technical concepts tailored to partners in marketing, sales, market access, finance, and legal/compliance.

Desired Qualifications

  • Experience with machine learning operations tools and practices, including continuous integration/continuous delivery, experiment tracking, and model monitoring, to maintain model stability and scalability.
  • Experience with generative artificial intelligence, large language models, and agentic models.

About the Company

Cytokinetics

Develops small-molecule therapies for muscle function

Cytokinetics develops and commercializes small molecule drugs aimed at improving muscle function for cardiovascular and neuromuscular diseases. Its products are designed to either enhance or adjust muscle contraction, with a pipeline that includes compounds like omecamtiv mecarbil, reldesemtiv, CK-274, AMG 594, and CK-601, currently in various stages of clinical trials. The company advances therapies through partnerships, licensing, and potential sales of approved medicines, and relies on extensive clinical testing to demonstrate effectiveness and safety. What sets Cytokinetics apart is its focused approach on muscle function modulators (activators and inhibitors) for both heart and skeletal muscle diseases, targeting conditions such as heart failure, hypertrophic cardiomyopathy, ALS, and SMA. Its goal is to bring medicines to patients to improve muscle performance and daily function, addressing unmet medical needs in these diseases.

Company size
501-1,000
Company stage
IPO
Headquarters
South San Francisco, CA
Founded
1998

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