About the role

Lead Data Scientist responsible for designing and deploying Generative AI solutions. Collaborating closely with clients and mentoring teams in analytics consulting at Tiger Analytics.

Responsibilities

Work on the latest applications of data science to solve business problems.
Work directly with client stakeholders to translate business problems into high level analytics solution designs.
Present analytic solutions to business audiences highlighting robustness of the solution and how it could help generate business value.
Develop end-to-end solutions based on in-depth understanding of business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably.
Design and develop machine learning and Generative AI solutions using RAG.
Build LLM-powered applications leveraging Azure OpenAI and orchestrate workflows using LangGraph.
Develop agentic AI workflows for automation, insights generation, and decision support.
Implement Document Intelligence solutions for extracting insights from unstructured data.
Participate in discussions with team members to select and apply relevant analytic techniques and create actionable business insights.
Responsible for making presentations to senior management, communicating results to business teams, and develop plans to help operationalize analytic solution.

7+ years of experience working as a GenAI Data Science.
Proficiency in Python and SQL.
Experience with MLflow and model lifecycle management.
Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git.
Generative AI Knowledge: Solid understanding of latest-generation AI concepts including LLMs, prompt engineering, retrieval-augmented generation (RAG), and other contemporary generative AI applications.
Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.).
Experience with Bedrock, JumpStart, HuggingFace.
Experience evaluating ethical implications of AI and controlling for them (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, etc.) as well as pre-trained models (e.g., LLaMa, SAM, etc.).
Experience developing models from inception to deployment 5-10 years of professional work experience with at least 5 years in Data Science.
Experience building end-to-end ML pipelines in production.
Familiarity with CI/CD pipelines, monitoring, and model governance.
Ability to design scalable and reliable AI systems.
Bachelor's in Business Analytics or equivalent work experience.

Significant career development opportunities exist as the company grows.
Unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
Equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.