{"id":5606,"date":"2024-02-28T19:06:44","date_gmt":"2024-02-28T19:06:44","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/?page_id=5606"},"modified":"2024-02-28T19:06:44","modified_gmt":"2024-02-28T19:06:44","slug":"prateek-shrivastava","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/prateek-shrivastava\/","title":{"rendered":"Prateek Shrivastava"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-5606\" data-postid=\"5606\" class=\"themify_builder_content themify_builder_content-5606 themify_builder tf_clear\">\n                    <div  data-lazy=\"1\" class=\"module_row themify_builder_row dropPadding-bottom tb_0l52602 tb_first tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_0ygc603 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_esex603 image-top   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"300\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/files\/2024\/02\/Prateek_Shrivastava.jpg\" class=\"wp-post-image wp-image-5543\" title=\"Prateek Shrivastava\" alt=\"Prateek Shrivastava\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/files\/2024\/02\/Prateek_Shrivastava.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics2024\/files\/2024\/02\/Prateek_Shrivastava-150x150.jpg 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Prateek Shrivastava                            <\/h3>\n                    <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_g2w3604 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_t7c7604   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h1>Prateek Shrivastava<\/h1>\n<h5>Principal Data Scientist at Cummins<\/h5>\n    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_2plz358 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  glossy tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-2plz358-0\" class=\"tb_title_accordion\" aria-controls=\"acc-2plz358-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-2plz358-0-content\" data-id=\"acc-2plz358-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Experienced Data Scientist with a remarkable 11-year track record in developing end-to-end data analytics solutions across diverse industries, utilizing expertise in Python, R, SQL, SAS, and Spark. Recognized as an analytics leader, I specialize in crafting intelligent solutions for complex business challenges.<\/p>\n<p>Currently serving as the Principal Data Scientist at Cummins, I&#8217;ve spearheaded transformative initiatives, including the design and implementation of a supplier warranty recovery process, yielding an annual revenue boost of over $3 million. As a team leader, I provide mentorship and oversight, fostering best practices and delivering high-quality work in the realm of quality analytics.<\/p>\n<p>In my role as Senior Data Scientist at 84.51\u00b0 (Kroger), I orchestrated the execution of Kroger&#8217;s largest consumer marketing mailer campaign, driving a shopping uplift of over $25 million per event. My contributions extend to developing modularized Python-based frameworks, executing A\/B tests, and providing key business insights through data analysis.<\/p>\n<p>During my tenure at the Big Data Insights Division DMI, I played a pivotal role as a Senior Statistical Analyst, contributing to revenue generation through automated pricing solutions and migration of critical reports. I have a proven track record of leveraging data analytics to identify cost-saving opportunities, present root-cause analyses, and build dashboards to aid decision-making.\u00a0<\/p>\n<p><strong>Education:<\/strong><\/p>\n<p>Armed with a Master of Science in Information Systems from the University of Cincinnati and a Bachelor of Engineering in Computer Science from Devi Ahilya University, India, my academic background complements my extensive professional experience.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-css_id=\"u6fe602\" data-lazy=\"1\" class=\"module_row themify_builder_row fullwidth_row_container tb_u6fe602 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_84q1605 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_k572608 track  \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h6>Track: Predictive Analytics<\/h6>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_xwmz608   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>Enhanced Association Rules Mining in Predictive Maintenance<\/h2>\n<p>This talk discusses Cummins&#8217; commitment to tackling engine failures in logistics. Cummins, a leading engine manufacturer, aims to leverage telematics technology to predict engine failures days in advance. The talk proposes using customized association rules mining techniques to analyze telematics data, establishing connections between diagnostic troubleshooting codes (DTCs) and various engine failures. The goal is to create a proactive predictive model that identifies impending engine issues. The study anticipates improving Cummins&#8217; maintenance strategies, reducing downtime, enhancing operational efficiency, and cutting costs. The study&#8217;s broader impact lies in contributing insights to predictive maintenance for heavy truck engines, emphasizing the transformative potential of technology in revolutionizing logistics industry practices.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Prateek Shrivastava Principal Data Scientist at Cummins<\/p>\n","protected":false},"author":1001140,"featured_media":0,"parent":712,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-5606","page","type-page","status-publish","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.0) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Prateek Shrivastava - 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Recognized as an analytics leader, I specialize in crafting intelligent solutions for complex business challenges.<\/p> <p>Currently serving as the Principal Data Scientist at Cummins, I've spearheaded transformative initiatives, including the design and implementation of a supplier warranty recovery process, yielding an annual revenue boost of over $3 million. As a team leader, I provide mentorship and oversight, fostering best practices and delivering high-quality work in the realm of quality analytics.<\/p> <p>In my role as Senior Data Scientist at 84.51\u00b0 (Kroger), I orchestrated the execution of Kroger's largest consumer marketing mailer campaign, driving a shopping uplift of over $25 million per event. My contributions extend to developing modularized Python-based frameworks, executing A\/B tests, and providing key business insights through data analysis.<\/p> <p>During my tenure at the Big Data Insights Division DMI, I played a pivotal role as a Senior Statistical Analyst, contributing to revenue generation through automated pricing solutions and migration of critical reports. I have a proven track record of leveraging data analytics to identify cost-saving opportunities, present root-cause analyses, and build dashboards to aid decision-making.\u00a0<\/p> <p><strong>Education:<\/strong><\/p> <p>Armed with a Master of Science in Information Systems from the University of Cincinnati and a Bachelor of Engineering in Computer Science from Devi Ahilya University, India, my academic background complements my extensive professional experience.<\/p><\/li><\/ul>\n<h6>Track: Predictive Analytics<\/h6>\n<h2>Enhanced Association Rules Mining in Predictive Maintenance<\/h2> <p>This talk discusses Cummins' commitment to tackling engine failures in logistics. Cummins, a leading engine manufacturer, aims to leverage telematics technology to predict engine failures days in advance. The talk proposes using customized association rules mining techniques to analyze telematics data, establishing connections between diagnostic troubleshooting codes (DTCs) and various engine failures. The goal is to create a proactive predictive model that identifies impending engine issues. The study anticipates improving Cummins' maintenance strategies, reducing downtime, enhancing operational efficiency, and cutting costs. The study's broader impact lies in contributing insights to predictive maintenance for heavy truck engines, emphasizing the transformative potential of technology in revolutionizing logistics industry practices.<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/5606","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/users\/1001140"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/comments?post=5606"}],"version-history":[{"count":2,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/5606\/revisions"}],"predecessor-version":[{"id":5608,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/5606\/revisions\/5608"}],"up":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/712"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/media?parent=5606"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}