C
Data Science Team Lead
Not specifiedFull-timeData ScienceCybersecurity
Lead the vision and development of generative AI capabilities for investigative intelligence software. Own the AI roadmap, guide a data science team, and deliver scalable production solutions using LLMs, agentic workflows, and classical machine learning.
Responsibilities
- Define the strategic AI roadmap and act as the domain authority for cross-functional initiatives.
- Lead a data science team developing a generative AI copilot and agentic capabilities.
- Architect LLM, generative AI, and autonomous workflow solutions for investigative intelligence use cases.
- Oversee the full AI and machine learning lifecycle from experimentation and prototyping through deployment, monitoring, and optimization.
- Partner with international product, engineering, and architecture teams to deliver scalable solutions.
Requirements
- Master’s degree or higher in computer science, mathematics, engineering, or a related quantitative field.
- At least 7 years of hands-on experience building and deploying production AI or machine learning solutions.
- At least 2 years of experience leading or growing a data science or machine learning team.
- Proven experience designing and shipping generative AI product features and multi-agent systems.
- Experience with orchestration frameworks such as LangGraph, CrewAI, or AutoGen, including retrieval-augmented generation, vector databases, and tool or function calling.
- Practical experience with AI observability and evaluation tooling, durable workflow orchestration, and typed service design.
- Strong knowledge of supervised and unsupervised learning, feature engineering, and model evaluation.
- Ability to determine when classical machine learning models are more appropriate than large language models.
Nice to have
- Experience fine-tuning and deploying language models using supervised fine-tuning, GRPO, or DPO.
- Experience with model optimization techniques including quantization, pruning, LoRA, and QLoRA.
- Experience with high-throughput inference frameworks such as vLLM.
- Experience designing cloud-native ML pipelines on Kubernetes, GCP, or AWS.