Principal Data Scientist

• Architect, develop, and productionize large-scale MachineLearning, Deep Learning, and Generative AI systems for consumer-facingapplications.
• Build personalization and recommendation systems usingtechniques such as Two-Tower architectures, neural retrieval, sequentialmodeling, representation learning, and learning-to-rank.
• Design, fine-tune, and post-train Large Language Models(LLMs) and Small Language Models (SLMs) using techniques such as SupervisedFine-Tuning (SFT), LoRA/PEFT, preference optimization, GRPO, RLHF, and RLVR.
• Design custom reward functions, reward models, andevaluation signals for reinforcement-learning-based LLM post-training,incorporating task-specific, quality, factuality, and preference signals.
  Develop embedding and representation-learning systemsusing contrastive learning, self-supervised learning, sequence modeling, andrelated deep-learning techniques.
• Build semantic search, retrieval, and ranking systemsleveraging dense retrieval, vector search, multi-vector retrieval, reranking,and modern neural Information Retrieval techniques.
• Explore and develop RAG, Graph RAG, Knowledge Graph,Agentic AI, tool-calling, and multi-step reasoning systems for complex AIapplications.
• Build scalable synthetic-data generation, dataaugmentation, and model-training pipelines for LLM and ML applications.
• Design robust offline and online evaluation frameworks forGenerative AI, retrieval, ranking, recommendation, and personalization systems,including A/B experimentation.
• Optimize AI/ML systems across model quality, inferencelatency, throughput, scalability, reliability, compute, and serving cost.
• Translate ambiguous business problems into well-defined MLproblems and independently drive them through problem formulation, research,experimentation, architecture, productionization, and measurement.
• Partner closely with Product, Engineering, Analytics, andData teams to deliver AI/ML capabilities with measurable customer and businessimpact.
• Provide technical leadership to a team of Data Scientiststhrough architecture reviews, technical guidance, mentoring, experimentationstrategy, and ML engineering best practices.
• Continuously evaluate emerging AI/ML research anddetermine its applicability to real-world problems and production systems.

• 8+ years of industry experience in Machine Learning, DataScience, Applied AI, NLP, Information Retrieval, Recommendation Systems, orrelated areas.
• Strong hands-on expertise in Python and moderndeep-learning frameworks such as PyTorch and/or TensorFlow.
• Deep understanding of Transformers, attention mechanisms,embeddings, representation learning, and modern deep-learning architectures.
• Strong expertise across multiple areas such as GenerativeAI/LLMs, Recommendation Systems, Personalization, Search Ranking, NLP,Information Retrieval, and Representation Learning.
• Hands-on experience across the modern LLM lifecycle,including SFT, PEFT techniques such as LoRA, preference optimization, rewardmodeling, RL-based post-training approaches such as GRPO/RLHF/RLVR, evaluation,and inference.
• Experience designing and deploying production-grade MLsystems at scale and working with large-scale behavioral, interaction, ortransactional datasets.
• Strong system-design skills with the ability to makepractical trade-offs across model quality, latency, compute, scalability,reliability, and cost.
• Demonstrated ability to provide technical direction andmentor Data Scientists and ML Engineers.
• Strong communication and collaboration skills, with theability to work effectively across Product, Engineering, Data, Analytics, andbusiness stakeholders.
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