Data Scientist, Actimize
NICE ActimizeIndia - PunePosted 7 April 2026
Job Description
At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.
So, what’s the role all about?
Within Actimize, the AI and Analytics Team is developing the next generation advanced analytical cloud platform that will harness the power of data to provide maximum accuracy for our clients’ Financial Crime programs. As part of the PaaS/SaaS development group, you as a data scientists will be responsible to develop, analyze and program cutting-edge algorithms that will be at the core of Actimize Premier fraud prevention and Anti-money Laundering applications for Actimize cloud-based solutions
How will you make an impact?
NICE Actimize is the largest and broadest provider of financial crime, risk and compliance solutions for regional and global financial institutions has been consistently ranked as number one in the space
At NICE Actimize, we recognize that every employee’s contributions are integral to our company’s growth and success. To find and acquire the best and brightest talent around the globe, we offer a challenging work environment, competitive compensation, and benefits, and rewarding career opportunities. Come share, grow and learn with us – you’ll be challenged, you’ll have fun and you’ll be part of a fast growing, highly respected organization.
This new SaaS platform will enable our customers (some of the biggest financial institutes around the world) to create solutions on the platform to fight financial crime.
Our behavioral analytics / machine-learning platform is the leader in providing Fraud Detection and Anti-money Laundering solutions. You will extend, develop, program and implement new algorithms and underlying mathematical theory, and use your analytics skills to help design and implement core components of our algorithms and models. You can have a daily, direct impact on the continued evolution of our innovative products.
Have you got what it takes?
Work with Specialist Data Scientist engineers, Software engineers, architects, and managers in the design process for software products and services; contribute to the implementation planning and estimation
Desire to balance theory with pragmatic problem-solving skills to develop and apply advanced algorithms to real world problems and data
Ability to work with product managers and engineers to translate requirements into design that can be implemented into code for both fraud detection, anti-money laundering and data visualization. This includes interfacing directly with other groups inside and outside of RD as needed
Passionate about being at the forefront of Fraud detection, Anti-Money Laundering (AML) and cyber-crime in general.
Ensure the intended design and quality levels are met through regular code reviews and testing of the software in-development.
Personally contribute high volumes of quality code that is delivered with that of your colleagues in regular releases and deployments.
Qualifications:
Experience: 2-4 Years
MS in Statistics, CS, Applied Mathematics, Electrical Engineering or other quantitative fields with focus on machine learning, mathematical algorithms, probability, and statistical modelling
2+ years of experience in algorithm development and statistical analysis of massive datasets, or equivalent coursework
Hands on experience with statistical analysis, visualization, and data mining tools such as R or Python
1-2 years of scripting experience (Python, Perl), writing SQL queries and working with databases (MySQL preferred).
Experienced working with Databases (MySQL/Postgres and MongoDB) at schema definition, query performance tuning, ORM
Experience developing scalable multi-tenant SaaS-based solutions.
Familiarity and/or experience with public cloud infrastructure and technologies such as Ama ... (truncated, view full listing at source)
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