Data Scientist - Fraud prevention Job at Subbyx (Milan)

Data Scientist - Fraud Detection

Are you looking for a new challenge? Fancy helping us shape the future of motor ...

Location

Spain , Madrid

Salary:

Not provided

Prima

Expiration Date

Until further notice

Requirements

Extensive experience (3+ years) in data science or applied machine learning, leading end-to-end initiatives in complex technical domains and delivering measurable business impact
Strong analytical and quantitative mindset able to translate data into insights, assess model performance, and guide decision-making with evidence and clarity
Structured problem solver skilled at framing ambiguous challenges, defining success metrics, and communicating results to both technical and non-technical audiences
Strong Python programming skills, especially in the data domain, and fluent use of Git
Knowledge of Spark, SQL or any other data-oriented programming language and proficiency with standard data wrangling and modeling libraries (Pandas, Sklearn, Keras, XGBoost, LightGBM, etc.)

Job Responsibility

Design and improve fraud detection systems that prioritize high-risk claims, optimizing the trade-off between detection accuracy and operational efficiency
Collaborate across teams and functions, working closely with fraud analysts, engineers, and product managers
Develop deep expertise in fraud dynamics, identifying emerging fraud patterns, understanding how they evolve across the insurance lifecycle, and translating these insights into effective detection and prevention strategies, for example identifying suspicious networks of related claims, synthetic entities, or anomalous behavioral patterns

What we offer

Hybrid working, with a mix of home and office days
Up to 30 days per year work from anywhere
Access to learning resources, mentorship and a growth plan tailored to you
Private healthcare
Gym discounts
Wellbeing programs
Mental health support

Fulltime

Staff Data Scientist - Fraud & Risk

We are seeking a skilled and motivated Staff Data Scientist to join our Fraud & ...

Location

United States

Salary:

140000.00 - 210000.00 USD / Year

Socure

Expiration Date

Until further notice

Requirements

Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field
or equivalent professional experience
8+ years of experience in data science, machine learning, or related fields, ideally in a high-growth tech or fintech environment
Experience in fraud prevention, risk modeling, or identity verification
Years of hands-on experience developing and deploying deep learning models (such as transformers, CNNs/RNNs, and graph learning)
Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images
Strong proficiency in Python, SQL, and major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
Deep understanding of machine learning algorithms, model evaluation techniques, and data pipeline development
Experience with model deployment and monitoring in production environments (specific experience with real-time model inferencing is a plus)
Demonstrated ability to proactively deliver complex outcomes, mentor others, and influence cross-functional decisions

Job Responsibility

Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges
Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images
Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments
Take ownership of project outcomes, data quality, and delivery timelines
proactively escalate issues and work collaboratively to resolve challenges
Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning
Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions
Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection
Present findings and recommendations to technical and executive stakeholders with clarity and influence
Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems

What we offer

Offers Equity
Offers Bonus

Fulltime

Senior Data Scientist, Fraud

Join us in building the future of finance. Our mission is to democratize finance...

Location

United States , Menlo Park

Salary:

187000.00 - 220000.00 USD / Year

Robinhood

Expiration Date

Until further notice

Requirements

5+ years of experience in data science or applied ML, with a focus on fraud detection or risk mitigation
Advanced proficiency in Python and SQL
experience with ML frameworks like XGBoost, LightGBM, or TensorFlow
Strong statistical acumen with experience in anomaly detection, pattern recognition, and A/B testing
Excellent communication skills and ability to influence decision-making across technical and non-technical audiences
A collaborative mindset and proactive approach to navigating ambiguity in fast-paced environments

Job Responsibility

Design and deploy fraud detection models to protect Robinhood users and assets in real time
Analyze behavioral data to uncover emerging fraud vectors and support rapid incident response
Develop robust data pipelines and monitoring systems to ensure model accuracy and reliability
Partner with engineering and product teams to implement safeguards and user-facing features
Guide experimentation strategy and contribute to long-term fraud prevention roadmap

What we offer

Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
100% paid health insurance for employees with 90% coverage for dependents
Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
Employer-paid life & disability insurance, fertility benefits, and mental health benefits
Time off to recharge including company holidays, paid time off, sick time, parental leave, and more
Exceptional office experience with catered meals, events, and comfortable workspaces

Fulltime

Fraud Data Scientist

As a Fraud Data Scientist at Barclays, you will be responsible for the developme...

Location

United Kingdom , Northampton

Salary:

Not provided

Barclays

Expiration Date

Until further notice

Requirements

A Degree in Mathematics, Statistics, Computer Science, or a related field (or equivalent work experience)
Experience in fraud detection, scam prevention, or cybersecurity, ideally in a financial services or banking environment
Proficiency in data analysis, with hands-on experience using tools and languages such as Python, R, SQL and machine learning frameworks

Job Responsibility

Identification, collection, extraction of data from various sources, including internal and external sources
Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis
Development and maintenance of efficient data pipelines for automated data acquisition and processing
Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data
Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities
Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science

Fulltime

Senior Data Scientist, Payments & Fraud

As a Senior Data Scientist specializing in Payments & Fraud, you will play a piv...

Location

United States; Canada

Salary:

150000.00 - 207000.00 USD / Year

Lime

Expiration Date

Until further notice

Requirements

MS or PhD in Statistics, Economics, Computer Science, Applied Mathematics, Operations Research, or a related quantitative field
5+ years of industry experience as a Data Scientist, with a focus on payments, fraud detection, risk mitigation, or user trust
Deep understanding of probability and statistics, including causal inference and experimental design
Proficiency in SQL and Python, with end-to-end experience with data—including querying, data manipulation, aggregation, analysis, visualization, and model deployment
Machine Learning & Modeling: Strong understanding of predictive modeling, price elasticity estimation, and customer segmentation techniques
Business Acumen: Strong intuition for how pricing and loyalty strategies impact revenue, customer behavior, and long-term retention
Strong communication skills with the ability to convey technical concepts to non-technical stakeholders

Job Responsibility

Design and evaluate strategies to reduce fraud and abuse across promotions, referrals, refunds, and payment flows
Develop and improve machine learning models, rules-based systems, and heuristics to detect high-risk behavior while minimizing false positives
Analyze experimental and observational data using A/B testing and causal inference to measure the effectiveness of interventions in payments and fraud prevention
Identify and quantify key friction points in the payments funnel (e.g., latency, failure rates, PSP routing), and partner with Engineering to improve reliability and speed
Build dashboards, reports, and self-serve tools to empower teams with visibility into fraud trends, payment performance, uncollected revenue, and risk tradeoffs
Collaborate on incident response and root cause analyses for fraud and abuse events, ensuring rapid mitigation and long-term prevention
Stay informed on evolving fraud tactics, risk mitigation techniques, and payment technologies to proactively shape Lime’s strategy

What we offer

Comprehensive Health & Wellness: A choice of medical, dental, and vision plans. We also provide company-paid life and disability insurance and company-funded mental health benefits
Financial & Retirement Planning: 401(k) plan with both pre-tax and Roth options, and access to a Health Savings Account (HSA) with a monthly company contribution
Family & Fertility Support: Paid parental leave for birthing and non-birthing parents, plus fertility and family-forming benefits
Paid Time Off: Unlimited vacation, paid leaves, and 10 company holidays
Unique Lime Perks: Complimentary use of Lime vehicles in participating cities, a monthly phone allowance, dedicated learning and development days, and access to perks including One Medical, Wellhub, and Headspace
Offers Equity
Offers Bonus

Fulltime

Data Scientist, Risk & Fraud

Data Scientist with expertise in fraud and risk to detect and prevent these thre...

Location

United States , San Francisco, CA; New York, NY; Los Angeles, CA; Seattle, WA

Salary:

185000.00 - 240000.00 USD / Year

Whatnot

Expiration Date

Until further notice

Requirements

5+ years of experience in the Data field
3+ years of experience in Data Analytics & Science supporting anti-fraud, risk, trust & safety, or integrity problems
Bachelor’s degree in Computer Science, Economics, Statistics, Cybersecurity, or a related field, or equivalent work experience
Industry experience with proven ability to apply scientific methods to solve real-world problems on large scale data
Advanced SQL skills and experience with modern data warehouses (Snowflake, BigQuery, Redshift) and tools like Spark or DBT
Proficiency with Python or R for data analysis, modeling, and experimentation
Experience designing and analyzing A/B tests and understanding causal inference techniques
Strong data visualization skills and familiarity with BI tools for building interactive dashboards
Ability to communicate complex ideas clearly, concisely, and impactfully across diverse stakeholders
Experience leading cross-functional projects and influencing trust & risk strategy with data

Job Responsibility

Translate complex data into actionable recommendations for the Fraud engineering and operations teams
Define and own the KPIs that measure the cost of fraud, strategies to prevent it, and impact to users and marketplace performance
Analyze the effectiveness of existing methods and partner with product and machine learning engineers to develop better anti-fraud practices
Partner with product managers, engineers, and operations teams to design, implement, and evaluate feature rollouts to combat bad actors on the platform
Define and own the experimentation playbook for Fraud at Whatnot
Develop frameworks for causal inference and impact measurement of efforts that are not well-suited to A/B testing
Ensure Whatnot’s internal KPIs treat fraudulent actors appropriately in measurement outside of fraud domains
Use our modern data stack to build dashboards, data pipelines, and self-serve tools that empower teams across Whatnot
Partner with engineers to improve data accessibility, ensure data quality, and support instrumentation for new product and platform enhancements
Advocate for data-driven decision-making and foster a culture of measurement across the trust & risk organization

What we offer

Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
Health Insurance options including Medical, Dental, Vision
Work From Home Support
Home office setup allowance
Monthly allowance for cell phone and internet
Care benefits
Monthly allowance for wellness
Annual allowance towards Childcare
Lifetime benefit for family planning, such as adoption or fertility expenses
Retirement

Fulltime

Data Scientist, North America Onboarding Team

As a Data Scientist on the North America Onboarding team, you will leverage your...

Location

United States , Austin

Salary:

Not provided

Assessfirst

Expiration Date

Until further notice

Requirements

Proven experience in a Data Scientist role, ideally with exposure to fraud detection, anti-money laundering (AML), or KYC/KYB domains within a FinTech or regulated business environment.
Strong proficiency in machine learning frameworks and Python programming language and are able to make and justify design decisions in your code. You know how to use Git to collaborate with others and are able to review code.
Expertise in data querying languages such as SQL, with experience working with large datasets and data processing technologies (e.g., Spark, Snowflake).
Familiarity with anomaly detection, supervised and unsupervised learning methods, and real-time risk scoring and data analysis.
Demonstrated ability to work collaboratively in cross-functional teams and effectively communicate complex technical concepts to non-technical stakeholders.
A strong product mindset with the ability to work independently in a cross-functional and cross-team environment.
Experience with statistical analysis and good presentation skills to drive insight into action.
Strong problem-solving skills with the ability to help refine problem statements and figure out how to solve them.
Familiarity with automating operational processes via technical solutions, for example Large Language Models (LLMs)

Job Responsibility

Design, develop, and deploy machine learning models to enhance our detection of financial crime, compliance violations, and risk associated with customer onboarding (KYC) and business verification (KYB).
Take over existing models to prevent chargebacks in North America. Ideate and work on new opportunities with ML to help reduce losses on chargebacks to reduce customer fees.
Analyze large volumes of customer and business data to identify trends, patterns, and anomalies related to identity verification and regulatory risk typologies.
Design and implement experiments (A/B tests) to evaluate the effectiveness of new risk controls and product features, continuously improving performance and balancing compliance with customer experience.
Develop robust data pipelines, algorithms, and tooling using Python and SQL to support real-time data ingestion and model scoring for the KYC/KYB process.
Collaborate with analysts, compliance teams, and engineers to translate complex business and regulatory requirements into actionable data insights and automated solutions.
Stay informed about the latest advancements in data science, machine learning, and regulatory compliance techniques to ensure state-of-the-art capabilities in the risk domain.

Senior Data Scientist - Digital / Device Intelligence

Socure is seeking a Senior Data Scientist to join our Digital Intelligence team....

Location

United States

Salary:

150000.00 - 185000.00 USD / Year

Socure

Expiration Date

Until further notice

Requirements

Master’s degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field
6+ years of experience in data science or applied machine learning, including experience working in production environments
Excellent SQL skills and extensive experience with large-scale databases and data modeling
Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data
Proficiency in Python and distributed computing tools (e.g., Spark, PySpark)
Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or similar
Excellent communication skills—able to explain complex technical results to non-technical stakeholders and senior leadership
Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques to assess model robustness
Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies
Strong judgment across data quality, model selection, and business impact tradeoffs

Job Responsibility

Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics
Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data)
Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse
Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques
Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning
Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust
Contribute to team standards for ML explainability, risk evaluation, and feature logging
Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences
Mentor junior data scientists and participate in cross-functional working groups

What we offer

Offers Equity
Offers Bonus
total rewards package includes equity, benefits, and an annual bonus or a commission plan
A collaborative and inclusive work environment that fosters learning and growth
Opportunities to grow into staff-level or technical leadership roles over time

Fulltime

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Data Scientist - Fraud prevention

Job Description

Job Responsibility

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