Dasar
Data Scientist
Diposting: 22.05.2026
Tanggal penutupan: 06.07.2026
Referensi pekerjaan: fcd9008e256ec16729a7f4966b6d8c18
Informasi pekerjaan
Lokasi
Lisboa, Lisbon Metropolitan Area, Portugal
Perusahaan
Jobio
Klien / Pemberi Kerja
Feedzai
Referensi pekerjaan
fcd9008e256ec16729a7f4966b6d8c18
Jenis daftar
Dasar
Izin kerja UE diperlukan
Tidak
Diposting
22.05.2026
Tanggal penutupan
06.07.2026
Deskripsi pekerjaan
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
Keterampilan
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