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We are seeking a motivated, curious, and meticulous Data Scientist to join our teams at Transcrime and Crime&tech. 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).
Job Responsibility:
Extracting, cleaning, transforming, and modelling corporate data (structured and semi-structured)
Building analytical datasets to support research projects, assessments, models, and reporting
Network and graph analysis to identify control structures, connections, and anomalous patterns among companies
Applying natural language processing (NLP) techniques to extract and classify information from texts (e.g. news, reports, corporate documents, open sources)
Developing and evaluating machine learning and AI models on corporate and textual data (feature engineering, selection and tuning of models, validation, interpretability)
Designing and developing descriptive and predictive risk assessment models (reputational risk, money laundering, and fraud)
Collaborating continuously with research, product, and development teams to devise data-driven and replicable solutions
Requirements:
Excellent knowledge of data analysis techniques (descriptive statistics, data exploration, data wrangling)
Experience with or strong interest in network and graph analysis
Familiarity with natural language processing (NLP) and the extraction/organisation of unstructured information
Proficiency in at least one of Python or R
Strong familiarity with SQL
Experience with machine learning and AI methods
Ability to communicate insights effectively to both technical and non-technical audiences
Good command of English
Nice to have:
Previous experience analysing corporate data (e.g. ownership structures, financial statements, company registries, adverse events)
Knowledge of major company data providers (e.g. Moody’s, Dun & Bradstreet, Sayari, Cerved)
Experience developing risk assessment models (credit and reputational) with corporate data
Experience with BigQuery and/or other data warehouse environments
Experience working with large datasets
Experience with data pipelines, ETL/ELT
Experience with social network analysis (SNA) and graph visualisation
Experience or interest in data visualisation (e.g. Rshiny, Power BI, Plotly)
Knowledge of machine learning frameworks or libraries
What we offer:
A young and dynamic environment that bridges academic research and real-world applications
Opportunities for growth and participation in international projects on economic-financial risks, corporate crime, and illicit markets
A contract and remuneration commensurate with profile and experience