Staff Software Engineer / Statistician, Early Stage X Project

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Job description

X is Alphabet's moonshot factory. We are a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world's most intractable problems, not just 10% improvement. We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup.About the team:We are an early stage X team focused on using causal machine learning to build predictive models that could transform the way many industries make decisions. You will be joining the founding team in a fast-paced, start up environment with a lot of room to grow your role with the team. We are a small team and there is plenty to do for someone interested in building cutting edge technology and building world-class predictive and actionable models.About the role:You will be leading the project's tech roadmap and setting direction for the ML team (including FTE, temporary, vendor, or contractor staff, AI Residents (ie, interns), by drawing on your early product development experience. You will be an active hands-on engineer on the project, building our predictive inference to accelerate us. We are looking for passionate and driven people, who are comfortable moving between creative, big-picture thinking and tactical execution in a fast-paced, fluid environment.How you will make 10x impact:Be a thought partner on the strategy and execution of the project. This will include refining the moonshot vision/hypothesis, as well as setting the long, medium, and short term milestones and tactics for the project.Be an active engineer and technical lead on the project. You must be willing to get your hands dirty!Build causal inference algorithms for our ML models of real-world systems (e.g. economics, agriculture, climate, transportation).Drive key decisions on software architecture and technology roadmap: balancing longevity with rapid prototyping; assess and scope technical feasibility of possible new products.What you should have:Software engineer, statistician or scientist with 6+ years of experience with causal inference and/or discovery algorithms and applications (e.g. time series analysis).Experience in several of the following: Applied Statistics, Causal Inference, Predictive Inference, Causal Discovery, Probabilistic Graphical Models, Treatment Effect Estimation, Counterfactual Estimation, Feature or Model Selection and Tuning in High Dimensional Settings.Experience with ML infrastructures as well as model design, development and optimization.Master's degree or PhD in a STEM field such as Statistics, CS, Physics, or Mathematics.An ability to thrive in unstructured work environments with rapidly changing requirements.It'd be great if you also had some of these:Startup / early product development experience.Demonstrated personal passion to apply talents towards solving big global problems.Experience using and iterating on ML models to solve real world problems, building models by using large diverse data sources, with different temporal and spatial resolutions.Model coupling, knowledge representation, dynamic modeling.Experience with using ML on time series data of dynamic systems.Openness to working across the stack - data pipelines to ML models deployment.Experience with some of these: TensorFlow, EconML, probabilistic programming libraries (e.g., TF Probability, pymc3, Stan)