Data Scientist III - LeapSpace
Actively Hiring
Full-time Posted 8 days ago
Role overview
- check_circle Develop and improve LLM-powered research workflows , including:Scientific question answeringLiterature summarizationSemantic exploration and discoveryResearch insight generationCitation-aware retrieval and reasoning workflows
- check_circle Build and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tools.
- check_circle Apply modern techniques in:NLPGenerative AIEmbeddings and semantic representationsRetrieval-augmented generation (RAG)AI reasoning and workflow orchestration
- check_circle Evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption.
- check_circle Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts.
- check_circle Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows.
- check_circle Design, develop, and optimize search and retrieval pipelines , including lexical, vector, and hybrid retrieval approaches.
- check_circle Contribute to the development and enhancement of RAG systems that integrate LLMs with trusted scientific and biomedical content.
- check_circle Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration techniques to improve relevance and answer quality.
- check_circle Support development of semantic search, ranking, and knowledge discovery capabilities.
- check_circle Collaborate with engineering teams to deploy and scale AI-powered solutions.
- check_circle Develop and apply 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 Build and maintain evaluation datasets, benchmark suites, and annotation workflows.
- check_circle Conduct offline experiments and contribute to online experimentation and A/B testing.
- check_circle Analyze experimental results and communicate findings to stakeholders.
- check_circle Contribute to responsible AI practices focused on quality, reliability, and trust.
- check_circle Partner with product managers, engineers, UX researchers, and domain experts to deliver AI-powered capabilities.
- check_circle Communicate technical findings and recommendations clearly to both technical and non-technical audiences.
- check_circle Contribute to knowledge sharing and adoption of best practices across the Platform Data Science organization.
- check_circle Support delivery of projects from research and experimentation through production deployment.
Basic qualifications
- Master’s or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
- Experience in data science, machine learning, applied NLP, information retrieval, generative AI, or a related field
- Hands-on experience with: LLM-based applications and generative AI systemsRAG pipelines and retrieval systemsSearch and retrieval architectures (lexical, vector, hybrid) Evaluation methodologies for IR and generative AI systems
- Strong 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 and machine learning platforms
- Understanding of experimentation methodologies, evaluation frameworks, and statistical analysis
- Proficiency with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn)
- Demonstrated ability to independently execute technical projects and contribute to cross-functional initiatives
Preferred qualifications
- Experience building AI assistants, agentic workflows, or conversational AI applications
- Experience working on search, ranking, recommendation, or retrieval 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
- Academic or industry research experience in NLP, information retrieval, search, or generative AI
- Experience working in content-rich, knowledge-intensive, or highly regulated domains
- Comprehensive Pension Plan
- Home, office, or commuting allowance.
- Generous vacation entitlement and option for sabbatical leave
- Maternity, Paternity, Adoption and Family Care leave
- Flexible working hours
- Personal Choice budget
- Internal communities and networks
- Various employee discounts
- Recruitment introduction reward
- Employee Assistance Program (global)
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
Tags & Focus Areas
Fulltime Ai Data Science Generative Ai
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