Principal Data Scientist (GenAI)

Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

As a Data Scientist, you will apply strong expertise in AI through the use of machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engines and services. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will develop efficient and accurate analytical models which mimic business decisions and incorporate those models into analytical data products and tools. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Key Responsibilities

• Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
• Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions.
• Stay up to date with industry advancements in AI and apply modern technologies and methodologies to our systems.
• Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as gcp, vertex ai, s3, AWS bedrock, Spark streaming, or similar.
• Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc.
• Ensure the reliability, availability, and scalability of data pipelines and systems through effective monitoring, alerting, and incident management.
• Implement best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies.
• Collaborate closely with DevOps and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems.
• Mentor junior team members and engage in communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns.
• Apply AI solutions to insurance-specific data use cases and challenges.
• Partner with architects and stakeholders to influence and implement the vision of the AI and data pipelines while safeguarding the integrity and scalability of the environment.