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Описание: |
Overview:
We are looking for a senior data scientist with deep expertise in causal inference and Bayesian network modeling to help build advanced analytical capabilities. In this role, you will lead the evaluation and implementation of Bayesian tools, support business-driven use cases, and collaborate across internal teams and external partners to drive strategic insights and decision-making.
Key Responsibilities:
* Modeling Leadership: Design and implement Bayesian networks and causal inference models to support complex business questions and R&D projects.
* Platform Evaluation: Assess Bayesian modeling tools using structured evaluation frameworks; guide platform selection and implementation.
* Capability Building: Support planning and funding processes for horizontal analytical capabilities, including hiring plans, vendor engagement, and tool acquisition.
* Stakeholder Collaboration: Work closely with data science, engineering, product, and business teams to align modeling efforts with organizational goals.
* Communication & Enablement: Translate technical findings into actionable insights for technical and non-technical stakeholders. Prepare clear documentation and presentations to drive adoption and understanding.
Basic Qualifications:
* Advanced degree (Master’s or PhD) in Statistics, Computer Science, Data Science, or a related quantitative field.
* Hands-on experience with Bayesian networks and causal inference methodologies, including model structure learning, parameter tuning, and evaluation.
* Familiarity with industry-leading tools such as GeNIe, Netica, Hugin Expert, BayesiaLab, AgenaRisk, Bayes Server, TETRAD, Bayes Suites, or CausaLens.
* Proficiency with Python or R, and strong foundations in statistical programming.
* Strong communication and project management skills; ability to lead cross-functional workstreams.
Preferred Qualifications:
* Experience in healthcare, pharmaceuticals, or life sciences domains.
* Background in evaluating and deploying analytics platforms within enterprise environments.
* Track record of leading or mentoring data science teams.
* Ability to align complex modeling work with strategic business needs.
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