This dissertation focuses on elite efforts to restructure work and education in the Russian data sciences. Russia has long had a strong national program in theoretical mathematics, but has been substantially less successful at applying this expertise to develop modern computational science, infrastructure, and business. As the Russian extraction economy stagnates, however, governmental, educational, and industry elites alike are looking to data science as a privileged locus for the translation of the so-called "human resources" of excellence in fundamental mathematics into the "human capital" of data-scientific expertise. Their reform efforts have brought together industrial and academic researchers in locally unprecedented ways, producing hybrid institutions, forms of pedagogy, and work practices that differ strikingly from those in other knowledge economies. Chapter One argues that data scientists understand their discipline as an inquiry into the properties and function of algorithms. It shows how their inquiry is governed not by a logic of truth and falsehood, but rather by the "efficiency" of a given algorithmic assemblage. Chapter Two charts the ecology of knowledge that supports this inquiry, highlighting the institutional arrangements that facilitate academic-industrial collaboration and the experience of a "data science community" in Moscow. Chapters Three and Four examine the novel pedagogical practices and forms of work that emerge as data scientists move back and forth between academic and industrial contexts. The pedagogical structures being developed at institutions like the Higher School of Education blend Western, neoliberal models of "professionalization" with the traditional forms of Russian mathematical education, offering a novel approach to training data scientists. Chapter Five explores the political and futurological imaginations of Muscovite data scientists, arguing that they can only be understood through reference to both the political economy of scientific research in contemporary Russia and the specific character of data scientific prediction. Together, these chapters provide an intimate look at a distinctly Russian form of computational modernity. As algorithmic processes and computational infrastructure increasingly form the bedrock of the international knowledge economy, understanding the institutional, pedagogical, and cultural dynamics surrounding their production will be Increasingly crucial for both science studies and the development of international science policy. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]