Data Science Lead
NewInactive
J
JobgetherAI / Compliance
Based in United StatesFull-TimeLead
This job is no longer active. We keep the page for reference, but the employer may not accept new applications.
Salary not disclosed
Job Details
- Required Skills
- PythonMachine LearningData scienceComplianceLLM
Requirements
- Strong hands-on experience in Data Science, applied Machine Learning, and production AI systems.
- Proven experience designing and deploying LLM-based solutions in production environments.
- Practical experience with Retrieval Augmented Generation architectures and LLM-driven retrieval workflows.
- Experience working with vector databases, embeddings, and embedding-generation pipelines.
- Strong Python programming skills and familiarity with modern machine learning and AI frameworks.
- Demonstrated experience designing model evaluation, benchmarking, and validation frameworks.
- Understanding of production-grade AI architecture, with an ability to move beyond proof-of-concept implementations.
- Experience operating in regulated, compliance-heavy, or otherwise highly governed environments.
- Strong ownership mindset, architectural judgment, and confidence making and influencing technical decisions.
- Excellent communication and collaboration skills, with the ability to work effectively across technical and non-technical teams.
- Experience with pharmaceutical, healthcare, or other regulated industries is a strong advantage.
- Familiarity with explainable AI methodologies, document intelligence, or NLP-heavy pipelines is beneficial.
Responsibilities
- Design, evolve, and oversee AI and machine learning architecture supporting automated compliance validation workflows.
- Design and optimize LLM-powered retrieval and validation pipelines, including Retrieval Augmented Generation (RAG) architectures.
- Establish evaluation frameworks, benchmarking approaches, and continuous improvement processes to measure and enhance AI system performance.
- Develop explainability, traceability, and validation mechanisms that support regulatory and compliance requirements.
- Collaborate closely with ML Engineers and Backend Engineers to productionize AI components and integrate them into reliable enterprise systems.
- Drive technical decisions around embeddings, vector databases, retrieval strategies, and related AI infrastructure.
- Ensure AI workflows are reproducible, testable, maintainable, and aligned with high-quality engineering standards.
- Support the scaling of AI capabilities from individual workflows into a robust, enterprise-grade platform.
- Lead technical discussions and align architectural decisions across product, engineering, data science, and compliance stakeholders.
- Identify opportunities to improve system reliability, model performance, scalability, and operational effectiveness.