Staff Data Scientist: Semantic Substrate Incubation
About The Position
At Semantic Substrate Incubation, we are drowning in data but starving for meaning. As our lead data scientist, you will bridge this "Meaning Gap" by turning raw, chaotic event logs into an intelligent, concept-linked graph—the Semantic Brain. You will move past basic chat interfaces to architect an Identity-Anchored World Model that allows LLMs to understand complex enterprise ideas like "High-Value Churn Risk" and drive autonomous, agentic decisions. Working alongside a tight-knit team of researchers and production engineers, your work will directly define how the next generation of AI comprehends the business world.
Requirements
- A Proven Tracker Record in AI/ML: Broad capability delivering high-impact AI systems at scale (typically requires around 10+ years of professional data science experience).
- Deep Graph Expertise: Hands-on experience designing, implementing, and querying graph databases, with specific, deep technical proficiency in AWS Neptune and SPARQL.
- Production-Level Data Pipelines: Extensive experience with Apache Spark (PySpark/Scala) for large-scale distributed data processing and ETL optimization on massive datasets.
- Modern LLM Orchestration: Direct, practical experience building sophisticated applications using frameworks like LangChain, LlamaIndex, or equivalent agentic workflows.
- Cloud Architecture: Strong hands-on backend and infrastructure skills utilizing Python and the AWS ecosystem (EC2, Lambda, S3, CloudFormation, CDK, or Terraform).
Responsibilities
- Map fragmented data to human-readable terms by leading the discovery and mapping of raw event logs to Vertical Ontologies (Industry Knowledge Packs).
- Accelerate AI accuracy by 60% by designing and deploying a Concept Graph that anchors the substrate, utilizing verified profile IDs instead of session data for memory.
- Train autonomous agents efficiently by building the logic for Reward Signal Extraction and Context-Aware actioning to infer KPIs directly from interaction logs, avoiding traditional delayed-reward bottlenecks.
- Reduce agentic action risk by 40% by utilizing Off-Policy Evaluation (OPE) and action-conditional world models to simulate high-value scenarios and ground recommendations.
- Avoid the "Services Trap" and enable scale by engineering automated systems that allow 80% of the team's context mapping to be executed seamlessly without manual intervention.
Benefits
- Dedicated Growth Time: We spend 10% of our time every quarter on individual engineering growth activities, research exploration, and passion projects.
- Continuous Learning Stipend: Receive an annual stipend for technical books, research papers, and subscriptions to keep your skills at the absolute cutting edge.
- Conference & Publication Support: Fully covered travel and attendance expenses when you are selected to present research or speak at major industry AI conferences.
- Top-Tier Health & Wellness: Comprehensive global health, dental, and vision coverage, alongside flexible time-off policies to ensure you stay energized.
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What This Job Offers
Job Type
Full-time
Career Level
Principal
Education Level
No Education Listed
