Investigate large datasets to identify fraud patterns and develop predictive features.
Build and evaluate machine learning models for fraud detection across account opening and identity risk.
Write clean Python/PySpark code and collaborate with teams to deploy models into production.
Experian is a global data and technology company powering opportunities for people and businesses worldwide, operating across financial services, healthcare, automotive, and more. They have an amazing team of 25,200 people in 32 countries with a people-first, inclusive culture.
Prototype, evaluate, and deploy machine learning models for fraud detection with ongoing monitoring.
Design experiments to balance customer experience and fraud loss reduction.
Build anomaly detection systems to surface novel fraud vectors before scaling.
Moniepoint is Africa’s fastest-growing fintech, processing billions of Naira monthly with over 10 million accounts. They prioritize employee well-being and foster an inclusive culture.
Partner with the Business Unit Lead to identify gaps in decisioning performance and implement solutions.
Build production machine learning models to identify fraud in collaboration with other data scientists.
Communicate complex ideas to a variety of audiences, from Customer Success to Sales.
Signifyd helps merchants confidently grow their businesses by building trusted relationships with customers. Their advanced technology creates frictionless shopping experiences, and they are trusted by thousands of merchants across over 100 countries.
Apply deep learning and foundation models to solve fraud detection and financial risk problems using large-scale behavioral and sequential datasets.
Take models through the complete ML lifecycle including data preparation, pretraining, deployment, and production optimization.
Collaborate with cross-functional teams and clients to translate model capabilities into actionable risk decisions.
The company is a fintech organization focused on fraud detection and financial risk using AI and deep learning. It operates with a remote-first culture and a collaborative, autonomous team.
Monitor transactions and customer accounts to identify and investigate suspicious or fraudulent activity.
Utilize fraud detection tools and SQL to analyze large datasets for patterns and anomalies.
Develop and implement fraud prevention strategies and stay updated on emerging threats.
Sezzle is a fintech company revolutionizing shopping with interest-free installment plans to financially empower the next generation. They are building an innovative, dynamic team passionate about creating a unique shopping journey.
Analyze fraud trends and risk signals to identify emerging threats and control gaps.
Design, tune, and evaluate fraud rules and detection strategies.
Partner with cross-functional teams to implement controls and improve outcomes.
Wave helps small businesses thrive so the heart of our communities beats stronger. They work in an environment buzzing with creative energy and inspiration, valuing diversity and inclusion.
Own the ML charter across identity resolution, fraud detection, risk scoring, and consumer intelligence, including strategy, roadmap, and delivery.
Build and grow a globally distributed team of ML engineers through hiring, coaching, and development.
Set the technical bar for model building, evaluation, deployment, and monitoring while holding the organization accountable.
Narvar is a platform that simplifies post-purchase experiences for retailers, driving customer loyalty through seamless interactions. With hubs in San Francisco, Atlanta, London, and Bangalore, the company serves over 125 million consumers and fosters a culture of low ego, high trust, and innovation.
Develop and optimize fraud prevention strategies using advanced analytics and machine learning.
Monitor fraud trends and client portfolios to identify emerging threats and recommend mitigation measures.
Serve as a fraud risk expert, providing analytical findings and recommendations to stakeholders.
The partner company specializes in fraud prevention and risk analytics for financial institutions, operating in a fast-paced payments environment. They are a remote-first organization with a focus on collaboration and work-life balance.
Build and ship ML and GenAI systems from research to production.
Communicate technical concepts clearly to non-technical stakeholders.
Foster an atmosphere of experimentation, continuous learning, and improvement.
Expel is a cybersecurity company that uses machine learning and generative AI to detect threats in customers' environments. They offer a small, highly transparent environment where your voice matters and decisions have direct impact.
Develop statistical and predictive models for credit-risk indicators, including delinquency and credit losses.
Perform exploratory data analysis and model versioning, backtesting, and performance monitoring.
Collaborate with business and technology stakeholders to translate complex problems into analytical solutions.
The company operates at the intersection of data science, financial analytics, and credit risk management. It fosters a culture of continuous improvement, rigorous analysis, collaboration, and innovation.
Deliver and communicate high-quality data-driven analyses to key stakeholders, providing actionable insights.
Access, cleanse, and analyze internal and external data to support B2B credit risk management and pricing strategies.
Conduct data exploration, validation, and audits to identify data quality issues and recommend improvements.
TreviPay provides a global B2B payments and invoicing network for sellers, offering integrations with eCommerce and ERP solutions. With 40 years of experience, they serve leaders in manufacturing, retail, and transportation, fostering a supportive, collaborative, entrepreneurial environment.