Data Scientist, Actimize

• 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.

Have you got what it takes?

• 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.

Qualifications & Skills

Core Skills

• 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.

Additional Desired Qualifications

• Experience in fraud analytics, financial crime, or risk management models.

• Knowledge of financial systems and data standards.

• Experience with containerized model development using Kubernetes.

• Exposure to banking or financial services domain.

What’s in it for you?

Join an ever-growing, market disrupting, global company where the teams – comprised of the best of the best – work in a fast-paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr!

Enjoy NiCE-FLEX!

At NiCE, we work according to the NiCE-FLEX hybrid model, which enables maximum flexibility: 2 days working from the office and 3 days of remote work, each week. Naturally, office days focus on face-to-face meetings, where teamwork and collaborative thinking generate innovation, new ideas, and a vibrant, interactive atmosphere.

Requisition Details

• Requisition ID: 11349

• Reporting into: Tech Manager, Actimize

• Role Type: Individual Contributor

About NiCE

NICE Ltd. (NASDAQ: NICE) software products are used by 25,000+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3+ billion financial transactions.

Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30+ countries.

NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.