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Mid LevelFull-timePythonAWSAzureKubernetesMachine LearningData ScienceAgileGitAIML
Salary Range
$16,150 - $21,850 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for talented and motivated Data Scientists who are passionate about solving complex fraud-related problems using advanced analytics and machine learning. You will join a high-performing analytics team at NICE Actimize, working on cutting-edge analytical solutions.
Key Responsibilities
- →Work with large, complex datasets to analyze fraud cases and identify inconsistencies.
- →Build, validate, and optimize machine learning models for fraud detection and prevention.
- →Research data patterns to predict fraudulent transactions and improve model performance.
- →Enhance existing models using advanced computational algorithms and techniques.
- →Develop compelling visualizations that help stakeholders understand trends and insights.
- →Collaborate with business teams, engineers, and stakeholders to deliver scalable analytical solutions.
- →Drive continuous improvement by staying updated with the latest advancements in Data Science and ML.
- →Communicate analytical findings clearly to both technical and non-technical audiences.
- →Participate in critical discussions, advocate technical solutions, and support model deployment.
- →Contribute to innovation forums and knowledge-sharing initiatives across NICE.
Requirements
- ✓2 to 4 years of relevant Data Science experience.
- ✓Strong analytical, problem-solving, and communication skills.
- ✓Ability to explain complex analytical concepts to non-technical stakeholders.
- ✓Experience working in agile, multi-disciplinary teams.
- ✓Self-driven, collaborative, and committed to delivering high-quality outcomes.
- ✓Advanced degree in Statistics, Mathematics, Computer Science, Engineering, or related fields.
- ✓Strong knowledge of statistical techniques (regression, feature selection, time series, etc.).
- ✓Proficiency in SQL and Excel.
- ✓Strong programming skills in Python (3.7+).
- ✓Hands-on experience with ML techniques (clustering, decision trees, boosting, etc.).
- ✓Experience developing and deploying classification and regression models at enterprise scale.
- ✓Understanding of logistic regression and regularization techniques.
- ✓Familiarity with ML-Ops frameworks or containerized environments (Kubernetes is a plus).
- ✓Experience troubleshooting production data and deployed models.
- ✓Exposure to cloud platforms (AWS, Azure preferred).
- ✓Experience with visualization and presenting insights clearly.
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