The Role of Product Data Science
<p>Data science organizations help companies leverage data to build better products, improve customer experiences and grow the business. Yet, data scientists can get pulled into many directions. Amongst the endless opportunities that come by their desks and questions that arise, where should they spend their time? This post sets a vision for the role of a data science organization and how the team can maximize its impact.</p>
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<h1>Project categories</h1>
<p>Here are the types of work that a data scientist does:</p>
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<p>(image by author)</p>
<p>- <strong>Descriptive:</strong> Data scientists and analysts are the experts in how customers use the product and can describe the product “in numbers”. Descriptive analysis can include describing user patterns, customer journeys, growth funnels, and more.</p>
<p>- <strong>Diagnostic:</strong> Through causal analysis, experimentation, customer journey research and cohorts, data scientists find the reason behind metric trend changes, and determine which leading indicators <em>cause</em> future outcomes.</p>
<p>- <strong>Predictive:</strong> Through forecasts, data scientists can help predict future outcomes, such as propensity models, projected metric growth, whether a customer will be satisfied or churn, and their future lifetime value.</p>
<p>- <strong>Prescriptive:</strong> With all these insights and expertise, data scientists are well positioned to drive strategic discussions recommending product & program changes, and influence the roadmap. This can be through initiating feature ideas, consulting on potential investments, or building ML models that recommend the next best action.</p>
<h1>Product development process</h1>
<p>Here is an overview of how these tasks come to life, as part of the product development process</p>
<p><a href="https://pub.towardsai.net/the-role-of-product-data-science-9384147fd91f">Click Here</a></p>