As a Data Steward, you will deliver daily insights and ensure alignment with Enterprise Data Governance processes. Key responsibilities include developing and implementing data governance policies, ensuring compliance, monitoring data quality, resolving issues, and collaborating with cross-functional teams.

The role involves managing credit policy integration and forecasting, performing variance analysis, and reporting insights to senior management, with a focus on credit card policies in the banking sector.

The Sr. Consultant/Manager - Credit Risk Policy role involves integrating credit policy decisions into forecasting, conducting variance analysis, and using various risk models. The candidate will also report key insights to senior management and must have hands-on coding skills within the banking and financial services sector, specifically in credit cards.

As a Lead Data Scientist, you will utilize AI expertise, focusing on machine learning and data mining to design advanced analytics engines and solutions. The role involves collaborating with cross-functional teams to develop analytical models, improve performance of generative AI products, and drive strategic initiatives within the organization.

As a Data Platform Engineer, you will lead the design and implementation of scalable data pipelines, mentor a team of engineers, and drive modernization efforts for data platforms. You will work with streaming technologies, AWS, and best practices in data engineering, to optimize performance and collaborate with cross-functional teams to deliver business value.

The Data Scientist will use expertise in AI and machine learning to create advanced analytics engines and services, communicate effectively with business partners, and lead collaborative analytic approaches. The role involves developing interpretable and scalable models and addressing various business challenges through innovative solutions.

The Data Engineering Recruitment Specialist will manage the end-to-end recruitment process, focusing on data analytics-related roles. Responsibilities include sourcing and screening candidates, maintaining candidate relationships, collaborating with hiring managers, and managing candidate information in the ATS. The role requires knowledge of recruitment strategies and strong organizational skills, along with experience in US hiring.

The Technology Partner at Tiger Analytics will provide thought leadership, client engagement, and technology solutions for large transformation initiatives. Responsibilities include mentoring talent, creating proposals, establishing data governance, and engaging with executive stakeholders.

The Lead Data Engineer at Tiger Analytics will guide the data engineering team, oversee programming activities, and implement data architecture patterns in Azure. The role emphasizes technical development with major tools and languages while leading discussions, managing risks, and ensuring project deliverables are met efficiently.

As a Senior Data Scientist, you will apply your expertise in AI and machine learning to develop and prototype advanced analytics engines. You will collaborate with business partners to define problems, develop analytical models, and integrate solutions into data products, while also educating on the value of data-driven decision-making.

As a Data Scientist, you will leverage AI and machine learning to design and build advanced analytics engines, collaborate with various teams to define technical problems, develop analytical models, and drive strategy by delivering business value through data insights.

This role involves developing and maintaining policies and standards ensuring compliance with legal and regulatory requirements. The Director will collaborate with stakeholders for policy creation and conduct audits to address risks. An emphasis on stakeholder communication and continuous improvement is essential.

The Business Analyst will work with credit and consumer lending stakeholders to evaluate processes, identify inefficiencies, and implement solutions. Responsibilities include cross-functional collaboration, project management, and translating technical data into actionable strategies.

The Consultant - FP&A will provide financial planning and analysis support, assisting in forecasting, reporting, and metrics. The role requires collaboration with finance teams, detailed financial data analysis, and effective communication to push projects and manage challenges.

The Senior Data Scientist will develop and deliver advanced analytics and ML solutions, optimize data pipelines, monitor ML infrastructure, and collaborate with data and ML engineers in a CI/CD environment, focusing on deploying AI-driven innovations.

The Product Manager will manage project development and implementation, ensuring on-time delivery and desired results. Responsibilities include orchestrating project activities, using Agile methodologies to develop product road maps, and overseeing a cross-functional team. The role requires excellent planning and communication skills, along with a strong background in application development and program management.

The Technical Project Manager will oversee project development and implementation for products and services, ensuring timely delivery and desired results. The role involves managing project activities, timelines, resources, and stakeholder communication while using Agile methodologies.

The Technical Project Manager will manage and deliver project-related activities, ensuring timely deployment and successful outcomes. Responsibilities include project planning, directing schedules, utilizing agile methodologies, leading cross-functional teams, and coordinating deliverables.

The role involves collaborating with clients to define business problems, leading data analysis and collection efforts, and deploying advanced analytics solutions. Responsibilities include stakeholder engagement, translating complex problems into analytical frameworks, and ensuring business value through data-driven insights.

The Machine Learning Engineer will design, develop, and maintain scalable machine learning systems and data pipelines. Responsibilities include collaborating with data engineers and scientists, writing production-quality code, troubleshooting ML model issues, and deploying data science solutions to enhance business value.