Top Hybrid Data & Analytics Jobs

As an Accreditations Analyst at Parallel Markets, you will review accredited investor applications, verify documentation, and communicate with clients to ensure a smooth onboarding process. You'll handle customer inquiries, assist in regulatory compliance reviews, and adapt to various operational roles in a dynamic start-up environment.

As a Project Analyst in Client Services, you will focus on improving operational workflows by analyzing data, identifying inefficiencies, and implementing process enhancements. You will collaborate with various stakeholders, maintain knowledge of transactional flows, and support the team in optimizing payment processes using agile methodologies.

As a Senior Data Engineer, you will design, implement, and maintain Data & Analytics Platform applications. Responsibilities include developing data pipelines and analytic processes, collaborating with project teams, and performing root-cause analysis to resolve complex issues.

The Staff Data Scientist is responsible for leading the development and deployment of advanced machine learning models to enhance fraud detection. The role involves collaboration with cross-functional teams to implement scalable solutions, fostering innovation, and mentoring junior members in machine learning techniques.

The Senior Data Scientist at Sensor Tower will focus on developing data modeling algorithms to analyze large data sets, ensuring data accuracy and reliability. They will create data models, utilize AI for workflow improvements, and deliver insights for digital marketing products. This role requires collaboration with engineers and other data scientists to drive product development and improvements.

The Senior/Lead Data Scientist in Marketing Analytics is responsible for building and maintaining predictive models, identifying patterns and trends in data, and enhancing business decision-making. The role involves developing complex analytic frameworks, generating ad-hoc analyses, and creating new analytic procedures, requiring advanced communication skills to relay results to non-technical audiences.

The Data Scientist will design and build generative AI solutions, develop and fine-tune machine learning models, analyze datasets, and integrate AI models into existing pipelines. Key responsibilities include optimizing AI models for cloud performance, staying updated on advancements in AI, and documenting models and processes for knowledge sharing.