Principal Data Scientist || Fulltime

Principal

What You'll Do

Develop LLM-powered intelligent experiences that interpret and generate insights

from both tabular and unstructured data.

Build and optimize personalized Q&A systems using large language models,

enabling context-aware responses tailored to user needs.

Design and enhance conversational talent recommendation systems, combining

autonomous agent architectures with personalized recommendation

algorithms.

Advance traditional recommendation systems by evolving them from simple ranked

lists to multi-topic, interactive experiences that better reflect user intent.

Construct multi-agent intelligent workflows that translate natural language inputs

into complex, goal-directed task sequences.

Collaborate within a highly cross-functional team, including data scientists,

machine learning engineers, product managers, and UX designers.

Partner with fellow data scientists to design, prototype, and iterate on AI/ML

models and system architectures.

Work closely with machine learning engineers to deploy, monitor, and optimize

scalable AI/ML solutions in production environments.

Collaborate with product managers to design intuitive user experiences, define

feedback loops, and analyze user telemetry to guide product improvements.

Engage in end-to-end AI/ML product development, from ideation to deployment,

while continually expanding your technical and product skillset.

Follow and help define robust development standards to ensure the creation of

trustworthy, safe, and responsible AI systems.

Contribute to internal and external AI/ML research through experimentation,

whitepapers, and collaboration with the broader AI community. What You'll Bring

Proven experience deploying high-risk NLP applications in real-world, production

environments—such as those involving regulatory compliance, privacy, safety, or

fairness.

Demonstrated ability to advance and implement Trustworthy AI and Responsible

ML practices, working cross-functionally with engineering, legal, policy, and

product stakeholders across a large enterprise.

Track record of mentoring and coaching junior data scientists, especially in

navigating ambiguous or novel problem spaces. Strong applied machine learning experience, with solid foundational knowledge in

statistics, optimization, and deep learning—preferably gained at leading

technology companies (e.g., Google, Meta, Microsoft) or AI-first startups.

Excellent communication skills with the ability to synthesize complex technical

work into accessible insights for executive briefings, research publications, and

external presentations.

Advanced proficiency in Python and common ML/DS libraries such as NumPy,

pandas, scikit-learn, as well as deep learning frameworks like TensorFlow,

PyTorch.

Experience designing and deploying scalable deep learning systems, including

neural network architecture optimization, model distillation, quantization, or on-

device inference.

Strong understanding of machine learning infrastructure, including experience

with Kubeflow, MLflow, Airflow is a plus. Bonus Skills:

Hands-on experience with Text-to-SQL or Text-to-Cypher based application, or the

design of modern recommender systems.

Experience developing or fine-tuning large language models (LLMs), including

prompt engineering, retrieval-augmented generation (RAG), or open-weight model

customization.

Publication history in top-tier ML/NLP conferences such as NeurIPS, ICML, ACL,

EMNLP, or ICLR