Senior Data Scientist I

Role overview

Responsibilities

• check_circle Lead prototyping and development of LLM-powered research workflows , including: Scientific question answering Literature summarization Semantic exploration and discovery Research insight generation Citation-aware reasoning workflows
• check_circle Scientific question answering
• check_circle Literature summarization
• check_circle Semantic exploration and discovery
• check_circle Research insight generation
• check_circle Citation-aware reasoning workflows
• check_circle Design and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tooling.
• check_circle Apply state-of-the-art techniques in: NLP Generative AI Embeddings and semantic representations Retrieval-augmented generation (RAG) AI reasoning and orchestration
• check_circle NLP
• check_circle Generative AI
• check_circle Embeddings and semantic representations
• check_circle Retrieval-augmented generation (RAG)
• check_circle AI reasoning and orchestration
• check_circle Rapidly evaluate emerging AI models, tooling, and frameworks to identify opportunities for product innovation.
• check_circle Translate applied AI research into scalable, production-oriented solutions that improve researcher productivity and trust.
• check_circle Contribute to experimentation around prompt engineering, context management, grounding strategies, and hallucination mitigation.
• check_circle Support integration of scientific metadata, ontologies, and knowledge assets into AI workflows.
• check_circle Design and optimize search and retrieval pipelines , including lexical, vector, and hybrid retrieval approaches.
• check_circle Develop and improve RAG systems that integrate LLMs with trusted scientific and biomedical content.
• check_circle Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration to improve relevance and answer quality.
• check_circle Build scalable workflows for semantic search and knowledge discovery.
• check_circle Collaborate closely with engineering teams to productionize AI and retrieval systems.
• check_circle Develop and evolve evaluation frameworks for search and AI systems, including: IR metrics (e.g., NDCG, recall, precision) LLM and RAG evaluation metrics (e.g., grounding, faithfulness, hallucination detection)
• check_circle IR metrics (e.g., NDCG, recall, precision)
• check_circle LLM and RAG evaluation metrics (e.g., grounding, faithfulness, hallucination detection)
• check_circle Design offline evaluation methodologies and contribute to online experimentation and A/B testing.
• check_circle Build and maintain evaluation datasets, benchmark suites, and annotation strategies.
• check_circle Drive rigorous experimentation to measure system improvements and user impact.
• check_circle Contribute to responsible AI practices, including quality, reliability, and trust evaluation.
• check_circle Partner with product managers, engineers, UX researchers, and domain experts to deliver impactful AI capabilities.
• check_circle Translate complex technical findings into actionable recommendations for stakeholders.
• check_circle Contribute to technical strategy and roadmap discussions for LeapSpace AI capabilities.

Basic qualifications

• Master’s or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
• ~3–5+ years of experience in applied AI, machine learning, NLP, or information retrieval
• Strong hands-on experience with: LLM-based applications and generative AI systems RAG pipelines and retrieval systems Search and retrieval architectures (lexical, vector, hybrid) Evaluation methodologies for IR and generative AI systems
• LLM-based applications and generative AI systems
• RAG pipelines and retrieval systems
• Search and retrieval architectures (lexical, vector, hybrid)
• Evaluation methodologies for IR and generative AI systems
• Advanced programming skills in Python
• Experience with modern AI/ML frameworks and tooling (e.g., PyTorch, Hugging Face, LangChain, LangGraph , Haystack)
• Experience working with Databricks or similar distributed data/ML platforms
• Strong understanding of experimentation design, evaluation frameworks, and statistical analysis
• Proficiency with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn)

Preferred qualifications

• Experience building AI assistants, agentic workflows, or conversational AI systems
• Experience working on large-scale search, ranking, or recommendation systems
• Familiarity with scientific, biomedical, or scholarly datasets
• Experience with knowledge graphs, ontologies, or semantic enrichment systems
• Exposure to production ML systems and MLOps practices
• Publications or applied research contributions in NLP, IR, search, or generative AI
• Experience building AI systems in regulated, high-trust, or content-rich domains

About the company

• check_circle Search and retrieval systems
• check_circle Generative AI and LLM applications
• check_circle AI evaluation and experimentation
• check_circle Semantic enrichment and knowledge systems
• check_circle Scalable AI platforms and intelligent workflows