{"id":33682,"date":"2025-11-18T13:00:24","date_gmt":"2025-11-18T12:00:24","guid":{"rendered":"https:\/\/www.transcrime.it\/posizioni\/data-scientist\/"},"modified":"2025-11-18T17:05:18","modified_gmt":"2025-11-18T16:05:18","slug":"data-scientist","status":"publish","type":"posizioni","link":"https:\/\/www.transcrime.it\/en\/positions\/data-scientist\/","title":{"rendered":"Data Scientist"},"content":{"rendered":"<p>We are seeking a motivated, curious, and meticulous Data Scientist to join our teams at Transcrime and <a href=\"https:\/\/crimetech.it\/\">Crime&amp;tech<\/a>. You will work with large-scale corporate and economic\/financial data, both structured and unstructured, helping to identify anomalous patterns, relational networks, and at-risk businesses, including through the use of NLP, machine learning, and AI techniques applied to text analysis (e.g. news and company documents).<\/p>\n<h2><strong>Who we are<\/strong><\/h2>\n<p>Transcrime is the Joint Research Centre on Innovation and Crime of the Universit\u00e0 Cattolica del Sacro Cuore. Crime&amp;tech is the spin-off company of Universit\u00e0 Cattolica\u2013Transcrime, which translates its academic research into analyses, models, and tools to assess, prevent and reduce crime and risks to economic security. We work on international research projects and applications concerning illicit markets, corruption, financial crime, reputational risk, and economic\/financial security.<\/p>\n<h2><strong>Main responsibilities<\/strong><\/h2>\n<ul>\n<li>Extracting, cleaning, transforming, and modelling corporate data (structured and semi-structured).<\/li>\n<li>Building analytical datasets to support research projects, assessments, models, and reporting.<\/li>\n<li>Network and graph analysis to identify control structures, connections, and anomalous patterns among companies.<\/li>\n<li>Applying natural language processing (NLP) techniques to extract and classify information from texts (e.g. news, reports, corporate documents, open sources).<\/li>\n<li>Developing and evaluating machine learning and AI models on corporate and textual data (feature engineering, selection and tuning of models, validation, interpretability).<\/li>\n<li>Designing and developing descriptive and predictive risk assessment models (reputational risk, money laundering, and fraud).<\/li>\n<li>Collaborating continuously with research, product, and development teams to devise data-driven and replicable solutions.<\/li>\n<\/ul>\n<h2><strong>Minimum requirements<\/strong><\/h2>\n<ul>\n<li>Excellent knowledge of data analysis techniques (descriptive statistics, data exploration, data wrangling).<\/li>\n<li>Experience with or strong interest in network and graph analysis.<\/li>\n<li>Familiarity with natural language processing (NLP) and the extraction\/organisation of unstructured information.<\/li>\n<li>Proficiency in at least one of Python or R.<\/li>\n<li>Strong familiarity with SQL.<\/li>\n<li>Experience with machine learning and AI methods.<\/li>\n<li>Ability to communicate insights effectively to both technical and non-technical audiences.<\/li>\n<li>Good command of English.<\/li>\n<\/ul>\n<h2><strong>Preferred requirements<\/strong><\/h2>\n<ul>\n<li>Previous experience analysing corporate data (e.g. ownership structures, financial statements, company registries, adverse events).<\/li>\n<li>Knowledge of major company data providers (e.g. Moody\u2019s, Dun &amp; Bradstreet, Sayari, Cerved).<\/li>\n<li>Experience developing risk assessment models (credit and reputational) with corporate data.<\/li>\n<li>Experience with BigQuery and\/or other data warehouse environments.<\/li>\n<li>Experience working with large datasets.<\/li>\n<li>Experience with data pipelines, ETL\/ELT.<\/li>\n<li>Experience with social network analysis (SNA) and graph visualisation.<\/li>\n<li>Experience or interest in data visualisation (e.g. Rshiny, Power BI, Plotly).<\/li>\n<li>Knowledge of machine learning frameworks or libraries.<\/li>\n<\/ul>\n<h2><strong>Soft skills<\/strong><\/h2>\n<ul>\n<li>Methodological rigour and attention to the quality and traceability of analyses.<\/li>\n<li>Ability to clearly document and communicate completed work.<\/li>\n<li>Interdisciplinary collaboration skills (researchers, developers, analysts, economists, sociologists, legal experts).<\/li>\n<\/ul>\n<h2><strong>What we offer<\/strong><\/h2>\n<ul>\n<li>A young and dynamic environment that bridges academic research and real-world applications;<\/li>\n<li>Opportunities for growth and participation in international projects on economic-financial risks, corporate crime, and illicit markets;<\/li>\n<li>A contract and remuneration commensurate with profile and experience;<\/li>\n<li>Workplace in Milan, with the possibility of partial remote work.<\/li>\n<\/ul>\n<h2><strong>How to apply<\/strong><\/h2>\n<p>Submit your application by email to <strong><u><a href=\"mailto:transcrime@unicatt.it\">transcrime@unicatt.it<\/a><\/u><\/strong> with the subject <strong>\u201cApplication \u2013 Data Scientist\u201d<\/strong>, attaching your <strong>CV and cover letter<\/strong>.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-33682","posizioni","type-posizioni","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Scientist - Transcrime<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.transcrime.it\/en\/positions\/data-scientist\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Scientist - Transcrime\" \/>\n<meta property=\"og:description\" content=\"We are seeking a motivated, curious, and meticulous Data Scientist to join our teams at Transcrime and Crime&amp;tech. 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