Podstawy

Data Scientist

Lisboa, Lisbon Metropolitan Area, Portugal Firma: Jobio Klient / Pracodawca: Feedzai
Opublikowano: 22.05.2026
Data zamknięcia: 06.07.2026
Referencje dotyczące stanowiska: fcd9008e256ec16729a7f4966b6d8c18

Informacje o stanowisku

Lokalizacja
Lisboa, Lisbon Metropolitan Area, Portugal
Firma
Jobio
Klient / Pracodawca
Feedzai
Referencje dotyczące stanowiska
fcd9008e256ec16729a7f4966b6d8c18
Typ wpisu
Podstawy
Wymagane pozwolenie na pracę UE
Nie
Opublikowano
22.05.2026
Data zamknięcia
06.07.2026

Opis stanowiska

Feedzai is the world’s first RiskOps platform for financial risk management, and the market leader in safeguarding global commerce with today’s most advanced cloud-based risk management platform, powered by machine learning and artificial intelligence. Feedzai is securing the transition to a cashless world while enabling digital trust in every transaction and payment type. The world’s largest banks, processors, and retailers trust Feedzai to protect trillions of dollars and manage risk while improving the customer experience for everyday users, without compromising privacy. Feedzai is a Series D company and has raised $282M to date. With a valuation of $2 billion, our technology protects 1 billion consumers and 90 billion transactions each year.The Data Science Team within Customer Success is highly engaged with our clients making use of their critical thinking skills with a business-focused mentality and customer-facing attitude. They activate, maintain, and support clients, develop models and rules, and train & enable them. In addition, they work cross-functionally with other departments (e.g., Research, Product, Marketing) in a collaborative team spirit spanning the globe to ensure we deliver best in class risk prevention solutions. Being on the frontline of fighting crime and protecting people from financial harm is incredibly inspiring to each of us. Join Us! Your Day to Day:Understanding the data which our clients provide to us;Cleaning that data and validating that it is correct;Preprocessing the data, usually by using a mixture of shell scripts and a programming language such as Python, Java, Scala, etcIteratively computing features and tuning parameters to improve the quality of the model;Communicating your findings to the project manager and assisting him/her in decision making on the Data Science part of the project;Work together with key stakeholders (data scientists, engineers, risk managers) from our clients;Work with other parts of the organization (Product, Research, etc.) to improve processes, best practices and tooling. You Have & You Know-how:MSc or PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics or related field;Proficient in Machine Learning (training and testing, avoiding overfit, etc.);Knowledge of Big Data technologies such as Spark, Hadoop and related;Proficiency in bash, Python and either Java or Scala;Knowledge of resource monitoring and runtime optimization (both at JVM and OS level);Knowledge of statistics or data visualization is a plus;Knowledge of tree based algorithms (Random Forests, XGBoost, LGBM) is a plus;Knowledge of Deep Learning algorithms is a plus;Ability to communicate your findings in a clear way. The Customer Success Team is responsible for delivering our product to our clients. This includes education, configuration, solution development, and risk strategy to enable our clients to address their pain points. We collaborate with our clients to ensure they have the right solution, build out a strategy and training plan for them, and then support them through each phase of our client lifecycle. We grow at a fast clip and believe no challenge is too big or too small. Therefore, we have an open environment that encourages us to lean in, try new things, and discover our potential. Join Us!#LI-remote #LI-BR1

Umiejętności

apply blended learning apply for research funding apply research ethics and scientific integrity principles in research activities build recommender systems Business Analytics Business Intelligence collect ICT data communicate with a non-scientific audience Computational Biology Computer Simulation conduct research across disciplines create data models Data Engineering data ethics Data Mining Data Models data quality assessment Data Science data visualisation software define data quality criteria deliver visual presentation of data demonstrate disciplinary expertise design database in the cloud design database scheme develop data processing applications develop professional network with researchers and scientists Digital Curation disseminate results to the scientific community draft scientific or academic papers and technical documentation empirical analysis establish data processes evaluate research activities execute analytical mathematical calculations Hadoop handle data samples Healthcare Analytics image recognition implement data quality processes increase the impact of science on policy and society information categorisation Information Extraction integrate gender dimension in research integrate ICT data interact professionally in research and professional environments interpret current data LDAP LINQ make data-driven decisions manage data manage data collection systems manage findable accessible interoperable and reusable data manage ICT data architecture manage ICT data classification manage intellectual property rights manage open publications manage personal professional development manage research data Marketing Analytics mathematical modelling MDX mentor individuals multidisciplinary research N1QL normalise data online analytical processing operate open source software perform data cleansing perform data mining perform project management perform scientific research promote open innovation in research promote the participation of citizens in scientific and research activities promote the transfer of knowledge publish academic research quantitative analysis query languages report analysis results Research Design resource description framework query language Scientific Computing scientific literature Social Network Analysis SPARQL speak different languages State Estimation statistical modeling techniques Statistics synthesise information teach in academic or vocational contexts think abstractly Unstructured Data use data processing techniques use databases use spreadsheets software visual presentation techniques write scientific publications XQuery

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