09-14, 15:10–15:40 (Europe/Amsterdam), Foo (main)
Data scientists in industry often have to wear many hats. They must navigate statistical validity, business acumen and strategic thinking, while also representing the end user. In this talk, we will talk about the pillars that make a metric the right one for a job, and how to choose appropriate Key Performance Indicators (KPIs) to drive product success and strategic gains.
Our presentation will traverse the relationship of data science skills in product strategy - embracing the multifaceted role of the data scientist and navigating the journey from user segmentation to making data-driven decisions.
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The Data Scientist's Hat Trick: We initiate by emphasising the assorted roles that a data scientist plays in today's business landscape - from being a statistician ensuring the accuracy and validity of data to a strategist driving business decisions. [5 mins]
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Choosing Significant Metrics: Next, we'll delve into the nuances of selecting the right metric for the job. Specifically, we’ll talk about the different pillars of metrics setting, for common data science responsibilities such as randomised controlled trials, offline evaluation, opportunity analysis etc. [7 mins]
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Setting The Right KPIs: Once metrics are defined, we'll venture into setting the correct KPIs - the small set of top line numbers that say if our venture is doing the job. [7 mins]
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Data-Driven Decision Making: Lastly, we'll elucidate how to leverage the data you've gathered to make informed, strategic decisions. This necessitates interpreting your metrics and KPIs, spotting trends, and making necessary adjustments to stay on course. [7 mins]
Incorporating real-world case studies, we'll demonstrate how these concepts intertwine to contribute to product success.
Learning Objectives:
* Appreciate the multifaceted role of a data scientist in driving product strategies.
* Learn to set realistic and challenging KPIs that align with your company's overarching objectives.
* Gain insights into leveraging data for informed decision-making and product strategy adjustments.
Who Should Attend:
This talk is aimed for data professionals, however anyone involved in shaping product strategy and making data-driven decisions could find this useful.
No previous knowledge expected
Data scientist (Data Lead) at Spotify. Dismal scientist by education. Advocating against pie charts since 2015. Self-proclaimed GIF connoisseur.
Data Scientist (Tech Lead) at Meta