At NICE Actimize, we fight financial crime. We detect, prevent, and investigate money laundering, fraud, and compliance violations with a holistic view of risk across organizations. As the market leader in financial crime, risk, and compliance, NICE Actimize has deep domain expertise and a global understanding of the threat’s organizations face. With a holistic, consolidated view across the enterprise and innovative, flexible technology, we help detect and prevent potential fraud, manage regulatory compliance, and identify money laundering threats quickly and accurately, protecting institutions against financial crime, regulatory and reputational risk.
As the lead of the DataOps team, provide data solutions for the Data Scientist user community to ensure that time to value for delivery is reduced. Work with the DS lead to automate the provisioning of data on the AWS platform. Work with the wider engineering team to understand the AWS ecosystem and provide solutions to monitor data quality on the AWS data lake.
Main Responsibilities and Deliverables:
- Work with Data Science Manager, architects and data scientists in the design process for provisioning of data for the user community; contribute to the implementation planning and estimation
- Analyze the data needs in Fraud and AML through existing Machine Learning models in place, monitor the incoming data into the data lake through monitoring dashboards, incorporate a process to address the gap on an ongoing basis. This includes interfacing directly with other groups inside and outside of R&D as needed.
- Lead the end-to-end implementation, automation and support of data pipelines for the Data Science user community, pro-active identification of monitoring artifacts using AWS tools and high degrees of responsiveness to any issue that come up throughout the complete lifecycle of the data provisioning.
- Ensure strong communication is maintained through regular updates and reports. Mentor and coach peer and junior engineers and champion best practices and encourage software craftsmanship.
- Lead the DataOps team to ensure that commitments are met to the stakeholders.
Experience: 7 to 10 Years
Qualifications / Education:
- Degree in computer science or equivalent from a reputed institute. Should have played the role of a Data Analyst in a Financial Domain.
- Exposure to Financial Crime domain with a specific focus on data requirements for business rules/Machine learning models.
- Prior experience as a Business Analyst/Data Analyst in an Analytics domain. E.g. ETL, Data warehousing, ML
- Worked in high performance, highly available and scalable systems – a must
- Experience in database development with SQL/NoSQL;
- Exposure to Big data technologies like Hadoop, EMR, HIVE, HDFS etc.
- Exposure to Amazon Web Services (AWS)
- Experience in Agile delivery, managing customer/stakeholder communication, planning and appreciation of technical aspects of data provisioning is required.
Other Preferred skills:
- Substantial capabilities in the Fraud and AML domain, analyses information and deduces conclusions within the professional space, seen as a professional by his team and peer community. Has high-attention to details and works well in a dynamic environment.
- Experience working in a modified Agile methodology development environment and using work item management tools like TFS/Jira.
- Exposure to automated provisioning of data for ML models
- Experience with Open Source Software (OSS) technology frameworks, platforms, and tools and microservices
- Knowledge and Experience with enterprise applications running on Linux – big advantage
- Excellent interpersonal skills, proven ability and passion to educate and mentor others in engineering best practices and patterns, team player.
- Ability to work under high pressure
- High attention to details and accuracy
- Very strong verbal and written English skills