About the Role
Tiger Analytics is seeking an experienced Lead Data Scientist with strong expertise in AI, particularly Generative AI, to join our advanced analytics consulting firm. In this role, you will be instrumental in designing, prototyping, and building next-generation advanced analytics engines and services. You will work on developing efficient and accurate analytical models, incorporating them into analytical data products and tools, and driving strategy by leveraging your analytical skills to ensure business value and communicate results. You will collaborate with cross-functional teams and business partners to define technical problem statements and hypotheses.
Key Responsibilities
- Collaborate with business partners to develop innovative solutions utilizing cutting-edge techniques and tools.
- Experiment with, evaluate, and create generative AI products for various tasks, including data extraction, document summarization, and other generative model applications.
- Utilize fine-tuning and advanced knowledge retrieval methods to enhance the performance of generative AI models on specific tasks.
- Evaluate model performance and implement necessary improvements.
- Work collaboratively with other scientists, data engineers, machine learning operations engineers, prompt engineers, and product owners to develop generative AI products.
- Engineer features by applying business acumen to combine disparate internal and external data sources.
- Share your passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods, and standards.
- Collaborate, coach, and learn with a growing team of experienced Data Scientists.
- Stay informed on external ideas through conferences and community engagements.
Requirements
- 8+ years of overall experience, with 5+ years specifically as a GenAI Data Scientist.
- Proficiency in Python from a functional programming paradigm, including managing dependencies, virtual environments, and version control (git).
- Experience with sequential algorithms (e.g., LSTM, RNN, transformer).
- Familiarity with Bedrock, JumpStart, HuggingFace.
- Experience evaluating ethical implications of AI and implementing controls (e.g., red-teaming).
- Expertise in supervised learning and unsupervised learning, along with experience in deep learning and transfer learning.
- Experience in generative algorithms (e.g., GAN, VAE) as well as pre-trained models (e.g., LLaMa, SAM).
- Experience developing models from inception to deployment.