GenAI Data Scientist

As a Senior Data Scientist, you will translate the needs of our cross-functional stakeholders into user-facing applications that leverage NLP techniques and large language models (LLMs). As a Sr. Data Scientist on our GenAI applications team, you will work on products like conversational search interfaces, chatbots, text summarizers, recommender engines, and more based on the needs of the constituents. You will partner with Product Managers, Machine Learning Engineers, Cloud Platform Engineers, and cross-functional partners to develop production-grade algorithms.

You will contribute to the creation, delivery, and production of specific data science, machine learning and AI products for internal stakeholders; directly mentor other data professionals, data analysts, and machine learning engineers. In conjunction with the key partners, you will make technical decisions on data, technology, and ways of working

Duties and Responsibilities:

• Architect the overall framework and infrastructure for GenAI products like search interfaces, bots, summarizers, etc. Develop and implement techniques to optimize model performance to meet specific product goals
• Collaborate closely with product management and engineering leads to align on technical roadmap. Guide engineering teams to effectively leverage LLM capabilities in product implementations
• Establish protocols and systems for building fair, accountable and transparent LLM-based applications. Lead efforts to proactively assess and mitigate risks due to model biases or failures
• Implement robust feedback pipelines, monitoring and corrections to ensure model safety
• Design and oversee curation of high-quality datasets tailored for LLM training for each product. Build data science pipelines from feature generation, data visualization and models evaluation; design the solution, build initial code and provide documentation with ways of working to maximize time to value and re-usability.
• Communicate clearly and effectively to technical and non-technical audiences, verbally and visually, to create understanding, engagement, and buy-in. Contribute novel research and analyses to leading academic conferences and journals.
• Identify trends and opportunities to drive innovation, both in what we do and how we do it; evaluate new data science, machine learning, and AI technologies and tools that can boost team performance, innovation and business value. Proactively analyze latest developments in large language models to deeply understand model capabilities, limitations, and best practices. Develop techniques to continually improve language understanding and model training
• Mentor and develop junior data scientists in state-of-the-art GenAI methods
• Set technical vision and lead initiatives to accelerate product impact through cutting-edge LLM innovations
• Complete other responsibilities as assigned.

Required Skills and Qualifications:

• Total Exp: 8 to 10yrs+
• Advanced degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline.
• Minimum of three years’ experience in developing machine learning models with a track record of creating meaningful business impact and working with multiple stakeholders.
• Minimum of five years’ experience with Python.
• Minimum of three year’s experience building production NLP and deep learning models using PyTorch/Tensorflow, along with using large language model architectures (BERT, GPT-3 etc.)
• Experience building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using Langchain and similar tools
• Experience establishing model guardrails and developing bias detection and mitigation techniques for AI applications
• Proficiency with various prompting techniques, with a clear understanding of tradeoffs between prompting and finetuning
• Experience with finetuning embedding models and tuning vector databases to improve performance of semantic search and retrieval systems
• Deep understanding of underlying fundamentals such as Transformers, Self-Attention mechanisms that form the theoretical foundation of LLMs
• Experience with cloud computing platforms and tools (AWS).
• Experience operationalizing end-to-end machine learning applications.