We are looking for an exceptional Data Science Team Lead to spearhead the vision and evolution of our next-gen GenAI Co-Pilot. You will lead a team building a game-changing AI product that revolutionizes how users interact with our software - delivering real-time intelligence, deep investigative insights, and autonomous next-action guidance at scale.
You will:
Define the strategic AI roadmap across the system, serving as the core domain authority for cross-functional initiatives.
Architect next-gen capabilities by leveraging cutting-edge LLMs, GenAI, and autonomous agentic workflows to help analysts make critical, real-world decisions.
Oversee the end-to-end AI/ML lifecycle, from experimentation and prototyping to deployment, monitoring, and optimization.
Partner closely with international product, engineering, and architecture teams to drive high-quality, scalable solutions.
Join a team that fears no technology and constantly tests the boundaries of investigative power.
Requirements: M.Sc. or higher in Computer Science, Mathematics, Engineering, or a related quantitative field.
7+ years of hands-on experience building and deploying ML/AI solutions in production, including 2+ years leading or growing a data science/ML team.
Proven experience designing and shipping GenAI-based product features and multi-agent systems using modern orchestration frameworks such as LangGraph, CrewAI, or AutoGen, including RAG, vector databases, and tool/function calling.
Practical experience with the agentic production stack: observability/eval tooling (e.g., Langfuse), durable workflow orchestration, and typed service design.
Strong foundation in classical machine learning - supervised/unsupervised methods, feature engineering, model evaluation - and sound judgment for when to use classical models over LLMs.
Advantage: Demonstrated experience fine-tuning and deploying small/large language models, including SFT and preference/RL-based methods such as GRPO or DPO.
Advantage: Experience with model optimization and efficient serving: quantization, pruning, LoRA/QLoRA, and high-throughput inference frameworks such as vLLM.
Advantage: Experience designing end-to-end ML pipelines (versioning, training/tuning, deployment, testing, monitoring) on cloud-native infrastructure (Kubernetes, GCP/AWS).
This position is open to all candidates.