{"id":7887,"date":"2025-10-27T07:02:24","date_gmt":"2025-10-27T07:02:24","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/10\/27\/whats_next_for_a_11_yoe_data_scientist\/"},"modified":"2025-10-27T07:02:24","modified_gmt":"2025-10-27T07:02:24","slug":"whats_next_for_a_11_yoe_data_scientist","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/10\/27\/whats_next_for_a_11_yoe_data_scientist\/","title":{"rendered":"What\u2019s next for a 11 YOE data scientist?"},"content":{"rendered":"<p>    What\u2019s next for a 11 YOE data scientist?<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>Hi folks, Hope you\u2019re having a great day wherever you are in the world.<\/p>\n<p>Context: I\u2019ve been in the data science industry for the past 11 years. I started my career in telecom, where I worked extensively on time series analysis and data cleaning using R, Java, and Pig.<\/p>\n<p>After about two years, I landed my first \u201cdata scientist\u201d role in a bank, and I\u2019ve been in the financial sector ever since. Over time, I picked up Python, Spark, and TensorFlow to build ML models for marketing analytics and recommendation systems. It was a really fun period \u2014 the industry wasn\u2019t as mature back then. I used to get ridiculously excited whenever new boosting algorithms came out (think XGBoost, CatBoost, LightGBM) and spent hours experimenting with ensemble techniques to squeeze out higher uplift.<\/p>\n<p>I also did quite a bit of statistical A\/B testing \u2014 not just basic t-tests, but full experiment design with power analysis, control-treatment stratification, and post-hoc validation to account for selection bias and seasonality effects. I enjoyed quantifying incremental lift properly, whether through classical hypothesis testing or uplift modeling frameworks, and working with business teams to translate those metrics into campaign ROI or customer conversion outcomes.<\/p>\n<p>Fast forward to today \u2014 I\u2019ve been at my current company for about two years. Every department now wants to apply Gen AI (and even \u201cagentic AI\u201d) even though we haven\u2019t truly tested or measured many real-world efficiency gains yet. I spend most of my time in meetings listening to people talk all day about AI. Then I head back to my table to do prompt engineering, data cleaning, testing, and evaluation. Honestly, it feels off-putting that even my business stakeholders can now write decent prompts. I don\u2019t feel like I\u2019m contributing much anymore. Sure, the surrounding processes are important \u2014 but they\u2019ve become mundane, repetitive busywork.<\/p>\n<p>I\u2019m feeling understimulated intellectually and overstimulated by meetings, requests, and routine tasks. Anyone else in the same boat? Does this feel like the end of a data science journey? Am I far too gone? It\u2019s been 11 years for me, and lately, I\u2019ve been seriously considering moving into education \u2014 somewhere I might actually feel like I\u2019m contributing again.<\/p>\n<\/p><\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/appleciderv\"> \/u\/appleciderv <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1od5zca\/whats_next_for_a_11_yoe_data_scientist\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1od5zca\/whats_next_for_a_11_yoe_data_scientist\/\">[comments]<\/a><\/span>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    \/u\/appleciderv<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1od5zca\/whats_next_for_a_11_yoe_data_scientist\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What\u2019s next for a 11 YOE data scientist? Hi folks, Hope you\u2019re having a great day wherever you are in the world. Context: I\u2019ve been in the data science industry for the past 11 years. I started my career in telecom, where I worked extensively on time series analysis and data cleaning using R, Java, [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,99],"tags":[84,108,4104],"class_list":["post-7887","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-datascience","tag-data","tag-my","tag-ve"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7887"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=7887"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7887\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=7887"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=7887"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=7887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}