The Infrastructure Quantitative Engineering group uses statistical and machine learning techniques to support the operations and enable the continued growth of Meta's infrastructure. We partner with teams supporting all of Meta's infrastructure, focusing on long-term strategic initiatives that make Meta infrastructure more efficient, reliable, and scalable. We are looking to grow our data science support for the Physical Modeling group. The research scientists of Physical Modeling work on a variety of projects from a global level to the nanoscale, in a variety of domains that include, but are not limited to; physics, numerical and analytical modeling, data science, machine learning, experimental physics/EE and interdisciplinary projects. A successful candidate is typically involved in such projects end-to-end as a "full-stack" data scientist.As a Research Data Scientist - Physical Modeling, you will need to develop subject matter expertise, build trust with partners, recognize the biggest opportunities, create and drive strategy, and leverage data science methodologies to solve hard problems. In your work, you may provide guidance and coordinate with other data scientists to help achieve the goals in broad areas of operation.
Research Data Scientist, Physical Modeling Responsibilities:
- Lead and collaborate on projects with a globally based team of researchers, data scientists, and engineers inside and outside of Meta.
- Work on interdisciplinary projects and teams and identify and explore interdisciplinary opportunities, in particular bridging between the physical sciences and data science and machine learning addressing challenges directly relevant to Meta.
- Ensure coordination of theirs and others' projects across related workflows, to maximize impact and avoid duplication and overlaps.
- Work cross-functionally as a strategic partner to define priorities and develop project roadmaps in synergy with partner teams. Build consensus and earn commitment from partners. Drive execution through fast iteration.
- Employ languages and tools like Python, R, SQL, and others to drive efficient data exploration and modeling.
- Identify how data science can be applied to improve, optimize, and expand Meta's infrastructure across a variety of domains, with emphasis on long-term and strategic initiatives.
- Build pragmatic, scalable, and statistically rigorous solutions by leveraging or developing statistical and machine learning methodologies.
- Generalize methodologies for broader application within and outside their domain.
- Lead and provide technical mentorship to data scientists, to ensure continuous up-leveling of our expertise.
- Share results internally and externally through means of publications, presentations and blog posts.
- 6+ years of experience doing quantitative analysis including experience with SQL, other programming languages (e.g, Python) or statistical/mathematical software (e.g, R, SAS, MATLAB)
- Experience in quantitative measurements of noisy data, through e.g. demonstrated track record in experimental physics, high energy physics, data science or a similar discipline
- 6+ years of experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis
- High proficiency in communicating projects to both technical and non-technical audiences
- Advanced Degree (MSc, PhD) in Physics, Electrical Engineering, Mechanical Engineering or a related technical field
- 4+ years experience developing production software systems such as data pipelines, deployed machine learning models, or dashboards
- Experience working on cross-functional teams and matrixed organizations
- Experience working on technologies spanning multiple disciplines. In particular, physics and data science and machine learning
- Demonstrated track record of starting and leading successful interdisciplinary research and engineering projects
- Experience in handling multiple competing priorities in a fast-paced environment
- 4+ years of experience doing complex quantitative analysis and working with distributed (i.e. Hive, Hadoop or similar databases) or highly complex datasets
- 2+ years experience leading teams of other data scientists
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