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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Bachelors/Masters in a quantitative discipline (e.g., AI, statistics, operations research, econometrics, computer science, engineering) with 10+ years (or PhD with 8+ years). We are looking for a Data Science Director who is passionate about analytics for large scale ML systems to help drive data-informed decisions for the Monetization Infrastructure team and help shape the long term strategic direction of the development of this system.
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A Bachelor's degree in a quantitative field (e.g., Statistics, Biostatistics, Epidemiology, Data Science, Computer Science, Applied Mathematics, Economics). The Data Scientist/Biostatistician will be expected to create, perform, and document quality assurance of datasets, including the creation and documentation of formal test plans for unit and integrated data integrity testing.
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Experience in data science and quantitative research. Proven publication record in Operations, Robotics, Machine Learning, Computer Vision, Computer Science, Natural Language Processing, or other related technical fields.
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This is a challenging opportunity for an early-career professional who is passionate about social science research and has a strong desire to expand and deepen skills in economic research and quantitative data analysis.
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Bachelor's or master's degree required in any discipline with strong record of academic success in quantitative and analytic coursework such as operations research, applied mathematics, management science, data science, statistics, econometrics or engineering.
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The Data Science director will partner with Engineering and Product leads across Meta Ads, and AI infrastructure to guide the strategic direction for one of the largest-scale ML infrastructure systems in the world, developing new ML infrastructure capabilities, improving the efficiency of the systems and adopting the ads data systems to meet privacy requirements.
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Post Graduate degree in quantitative/relevant field such as Statistics, Operational Research, Computer Science, Economics, Business Analytics, Data Science, Business Administration, etc.
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Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Project Management, Software Development, Stakeholder Relationship Management.
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Director, Data Science - Monetization Infrastructure. The ideal candidate has experience with large-scale systems and data product background. Inspire, lead and grow a team of data scientists and managers across multiple teams.
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The Research Scientist must have training and experience in quantitative research and will design, plan and implement in-depth longitudinal studies with support from a staff of several researchers and data analysts.
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Ability to analyze and interpret data from diverse sources (qualitative and quantitative, primary and secondary), and synthesize complex information to provide actionable recommendations.
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Must have a PhD (or be a PhD candidate) in operations research, statistics, computer science, mathematics, engineering or a quantitative social science with a record of accomplishments and demonstrated research experience in constructing statistical models from real data, stochastic and deterministic modeling and simulation, optimization, numerical methods, algorithm development, data processing, and/or design of experiments.
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2 years of experience in data/analytics or functional business experience within the respective industry of responsibility (i.e. P&C, Bank, Finance) OR Advanced degree in quantitative analytics field such as Economics, Finance, Statistics, Mathematics, Actuarial Sciences, Operations Research, Data and/or Business Analysis, Data Science or other quantitative discipline.
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Developing and executing model-based analyses including translational PK/PD approaches, population pharmacokinetic models, exposure-response models, clinical trial design simulation, disease progression models, quantitative systems pharmacology (QSP) modeling into programs, comparator modeling, thereby strengthening our pharmacometrics capabilities on a continuous basis in decision making and driving pipeline impact.
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