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<h4 data-prosemirror-content-type="node" data-prosemirror-node-name="heading" data-prosemirror-node-block="true" data-pm-slice="1 1 []"><strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">The Opportunity</strong></h4> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">We are looking for a Stats Engineering leader to help us build two new Datadog products from scratch - Feature Flags and Experiments. </p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">Our goal with these products is to help developers and product teams ship features quickly, experiment as second-nature, and make decisions with confidence. To do this, we need to build a world-class experimentation engine, backed by state-of-the-art statistical methods like sequential analysis, CUPED, and change point detection, which help to solve the big problems in the experimentation world of early peeking, long experiment durations, and catching bugs respectively. </p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">Because there is often a technical, cultural, and linguistic gap between software engineers and statisticians, our Stats Engineers are unique in that they don't squarely fall into data science or software engineering. Data scientists typically understand the concepts, but may struggle to implement them in production with enterprise grade quality. Software engineers know how to build robust production systems, but can get lost implementing methods that don't have off the shelf frameworks. Here, we need the rare breed of builder who understands statistical concepts and can implement them in production.</p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">Our experimentation platform will be used for root-cause