Data Scientist, Actimize- Statistics, Applied Mathematics

What's in it for you?

Data Scientist

Actimize Premier is seeking a Data Scientist / Analyst (Statistics, Applied Mathematics- Mandatory) to design, develop, and optimize cutting-edge algorithms and machine learning solutions for financial fraud prevention and anti-money laundering (AML) applications. You will work on behavioral analytics and machine learning models while mentoring junior team members and collaborating closely with cross-functional teams. This role provides an opportunity to contribute to innovative, impactful products at the forefront of financial crime prevention technology.

Key Responsibilities:

• Develop and optimize advanced machine learning models and algorithms for fraud detection and AML applications.
• Mentor and guide junior data scientists and analysts, fostering a collaborative and high-performance team environment.
• Leverage cloud platforms (AWS, Azure, Google Cloud) to implement scalable AI/ML solutions.
• Contribute to the design and implementation of core algorithms, mathematical models, and data-driven solutions.
• Explore and apply emerging technologies such as Generative AI to enhance fraud detection capabilities.
• Collaborate with product managers, engineers, and other stakeholders to translate business requirements into robust technical solutions.
• Perform statistical analysis, data mining, and visualization using tools like Python or R.
• Drive innovation by researching and integrating the latest advancements in data science and machine learning.
• Support the team in building user behavior models, leveraging Bayesian statistics, and exploring advanced techniques like social network analysis.

Skills and Experience Required:

Educational Background:

• Master’s or Ph.D. in Statistics, Applied Mathematics, Data Science, Computer Science, Electrical Engineering, or a related quantitative field.

Professional Experience:

• 2 –4 years of experience in algorithm development, statistical analysis, and machine learning.
• Hands-on experience in applying advanced machine learning techniques to real-world datasets in financial fraud prevention, AML, or similar domains.

Technical Expertise:

• Proficiency in Python for statistical analysis, data modeling, and visualization.
• Experience with cloud technologies and platforms (AWS, Azure, or Google Cloud).
• Solid understanding of databases and SQL (e.g., MySQL).
• Exposure to generative AI techniques and their applications in data science.

Soft Skills and Teamwork:

• Strong mentoring and leadership skills, with a proven ability to guide and develop junior team members.
• Excellent problem-solving skills with a pragmatic approach to balancing theory and practical application.
• Effective communication skills to collaborate across teams and present complex ideas to stakeholders.
• Resourceful, adaptable, and passionate about financial crime prevention technologies.

Preferred Qualifications:

• Knowledge of user behavior modeling and Bayesian statistics.
• Experience in natural language processing (NLP).
• Familiarity with tools and libraries for generative AI (e.g., Transformer models).
• Understanding of the financial crime prevention domain and its associated challenges.

Why Join Us?

At Actimize Premier, you will play a critical role in developing industry-leading solutions to combat financial fraud and money laundering. This role offers the opportunity to work on innovative technologies, mentor a talented team, and make a tangible impact in the fight against financial crime. Join us to lead the evolution of AI-driven fraud detection and AML technologies.

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 ID: 8013

Reporting into: Tech Manager

Role Type: Individual Contributor