• Understand business needs, provide actionable insights and recommendations based on analyses, and translate them into technical requirements and solution proposals. • Stay up to date with trends and advances in data science. • Apply pattern recognition techniques, including but not limited to computer vision, convolutional neural networks (CNNs), language models for natural language processing (NLP), large language models (LLMs), data analysis and predictive analytics (ML). • Ensure technical feasibility of proposed machine learning models, partnering with data scientists in designing and building solutions. • Test and validate data science solutions to ensure their effectiveness, quality, and compliance with requirements. • Engage the team and foster constructive discussions, extracting information, knowledge, and potential from the group toward a shared objective or larger outcome. • Help teams increase productivity, achieve better results, solve complex organizational problems, respond quickly to change, and strengthen their organizational culture.

• Build agent workflows that take an integration need from plan to implementation and validation with humans approving at the right checkpoints. • Create systems that synthesize context from codebases, docs, specs, telemetry, and historical incidents to make changes that match Datadog conventions and customer expectations. • Generate and evolve integration code and tests, including end-to-end scenarios that reflect real customer workloads and product features. • Design evaluation harnesses that prevent silent regressions: golden sets, scenario baselines, semantic checks, performance thresholds, and release gating. • Build portfolio-level automation: proactive updates for upstream breaking changes, tracer feature rollouts across the catalog, migrations to new schemas/semantics, and targeted coverage expansion. • Partner tightly with PM, support engineers, and integration-owning teams to make the system adoptable, trustworthy, and embedded in daily engineering workflows.

• Build agent workflows that take an integration need from plan to implementation and validation with humans approving at the right checkpoints. • Create systems that synthesize context from codebases, docs, specs, telemetry, and historical incidents to make changes that match Datadog conventions and customer expectations. • Generate and evolve integration code and tests, including end-to-end scenarios that reflect real customer workloads and product features. • Design evaluation harnesses that prevent silent regressions: golden sets, scenario baselines, semantic checks, performance thresholds, and release gating. • Build portfolio-level automation: proactive updates for upstream breaking changes, tracer feature rollouts across the catalog, migrations to new schemas/semantics, and targeted coverage expansion. • Partner tightly with PM, support engineers, and integration-owning teams to make the system adoptable, trustworthy, and embedded in daily engineering workflows.

• Designing, implementing, and deploying end-to-end NLP and deep learning systems • Building LLM-powered applications that interact with real users • Developing and maintaining production Python services • Exposing models and pipelines via REST APIs (FastAPI, Flask) • Working on retrieval models and techniques (RAG, embeddings, ranking) • Evaluating, monitoring, and continuously improving model and system quality • Scaling systems to handle enormous volumes of requests