Overview
Skills
Job Details
As a Senior Data Scientist, you will collaborate with the Data Science and Machine Learning
team and will create data science, machine learning, and AI solutions to better address the
needs of our constituents.
You will have the chance to guide and continuously improve the ways in which we engage,
educate, and empower people around the world, combining the best of human touch and
technology scale. You will experiment with everything from the latest AI algorithms and
techniques to blended and immersive environments, multi-modal and varied-form content, and
the most innovative research and teaching methodologies. You will be highly influential in
advancing our LLM applications and guide teams towards impactful and ethical AI. We seek an
expert who is eager to grow and disseminate GenAI model expertise across the organization.
In this role, 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.
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 cuttingedge LLM innovations
Complete other responsibilities as assigned.
Required Skills and Qualifications:
Minimum of nine years post-secondary education or relevant work experience
Advanced degree in mathematics, physics, computer science, engineering, statistics, or
an equivalent technical discipline desired
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 years' 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, Google Cloud Platform, or other)
Experience operationalizing end-to-end machine learning applications