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Senior Data Scientist - Risk & Fraud

Hard Rock Digital
Full-time
Remote
Canada, Canada

Job description

What are we building?

Hard Rock Digital is focused on becoming the best online sportsbook, casino, and social casino company in the world. We’re a team passionate about crafting exceptional digital experiences for millions of customers. Rooted in the storied legacy of Hard Rock and the Seminole Tribe of Florida, we bring trust, authenticity, and high-quality entertainment into the digital gaming space—ready to join us?

What’s the position?

We are seeking a Sr. Data Scientist - Risk & Fraud to advance and refine our fraud detection and risk mitigation capabilities. In this role, you will combine a variety of modeling techniques—ranging from Logistic Regression, Ensemble Methods, and Deep Neural Networks to Clustering and Graph-based methods (e.g., Graph Neural Networks)—to detect complex fraud patterns and emerging threats. You’ll leverage your deep understanding of AML (Anti-Money Laundering) and related compliance policies, and collaborate with engineering, product, legal, and compliance teams to protect our customers and our platform from sophisticated illicit activity.

Responsibilities:

·      Advanced Model Development:

o   Architect, train, and deploy ML models (Logistic Regression, Gradient Boosted Trees, Random Forests, DNNs, Clustering) and graph-based algorithms to identify and prevent a wide array of fraud vectors.

o   Leverage graph analysis and Graph Neural Networks to uncover hidden relationships, detect fraud rings, and identify organized networks of malicious actors.

·      Data Engineering & Analysis:

o   Work with data engineering teams to build scalable data pipelines, integrating transactional, behavioral, and external intelligence data sources into robust, feature-rich datasets.

o   Employ statistical and causal inference techniques to deeply understand anomaly patterns and inform effective, data-driven preventive measures.

·      Operationalization & Monitoring:

o   Partner with engineering to implement end-to-end MLOps pipelines, ensuring models are reliably deployed, continuously monitored, and automatically retrained to address data drift and evolving threat landscapes.

o   Maintain real-time dashboards and metrics to track key performance indicators (e.g., fraud catch rates, precision, recall), ensuring timely detection and swift response to suspicious activities.

·      Policy & Compliance Alignment:

o   Collaborate with compliance, legal, and product teams to ensure modeling strategies align with AML regulations, gaming compliance standards, and industry best practices.

o   Use insights from risk models to suggest policy changes, influencing fraud prevention strategies that minimize false positives while maintaining stringent compliance.

·      Strategic Collaboration & Communication:

o   Communicate complex analytical findings, model insights, and recommended strategies to technical and non-technical stakeholders.

o   Inspire and mentor team members, fostering a culture of innovation, knowledge-sharing, and continuous improvement in fraud detection methods.

·      Innovation & Scaling:

o   Stay current with cutting-edge techniques in fraud detection, including GNNs, advanced anomaly detection, and the integration of AI agents or LLM-based solutions for rapid response and automated triaging of cases.

o   Continuously evaluate and incorporate new tools, techniques, and frameworks to enhance the scalability, reliability, and sophistication of our detection systems.

Job requirements

Qualifications (Required):

·      Advanced degree (Master’s or Ph.D.) in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Data Science) or equivalent professional experience.

·      6+ years of hands-on experience in data science or machine learning, with a strong emphasis on fraud detection, risk management, or financial security.

·      Proven expertise in diverse ML techniques (Logistic Regression, Ensemble Methods, DNNs, Clustering), and experience applying graph-based analytics or Graph Neural Networks to detect complex fraud patterns.

·      Strong programming skills in Python (or R) and SQL(Snowflake/DBT), with proficiency in ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and distributed data tools.

·      In-depth knowledge of AML regulations, as well as familiarity with other relevant compliance frameworks, fraud policies, and domain-specific requirements.

·      Solid understanding of statistical and causal inference methods for detecting rare events and addressing highly imbalanced datasets.

Good-to-Haves:

·      Experience in regulated industries, financial services, or subscription-based businesses with direct exposure to AML, KYC, or other compliance-related fraud detection challenges.

·      Familiarity with AI agents, LLM-based techniques, and anomaly detection tools to expedite investigations and root cause analysis.

·      Proficiency with data visualization tools (Tableau, PowerBI) to craft compelling narratives and influence strategic decision-making.

·      Exposure to modern DevOps/MLOps best practices (CI/CD, containerization, model governance in AWS) for sustainable, scalable model operations.

What’s in it for you?

·      Competitive pay and benefits

·      Flexible vacation allowance

·      A startup culture backed by a trusted, global brand

Roster of Uniques

We believe in fostering a diverse, inclusive environment where you can bring your whole self to work. We empower our teams to champion authenticity, innovation, and collaboration—join us and help shape the future of digital gaming.

 

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