Lead Data Scientist

structured output contracts. Define evaluation strategy: Create a rigorous eval suite covering answer correctness, attribution/grounding, toxicity/safety, privacy leakage, determinism, latency, and cost. Formalize LLMOps: Versioning for prompts/datasets/models, experiment governance, prompt and dataset registries, and promotion criteria from dev to staging to prod. Drive tool/agent design: API schema design for function calling, error handling, recovery strategies, self-correction, and guardrail integration. Make build-vs-buy calls: Weigh managed providers vs. open-source/self-hosted, considering performance, cost, IP, privacy, and compliance. Mentoring: Provide deep technical mentorship on prompting, retrieval design, evals, and safe deployment; lead reviews of prompts, pipelines, and evaluation reports. Hands-on Data Science Implement end-to-end RAG systems: ingestion, chunking, embeddings, hybrid search, rerank, prompt assembly, tool calls, post-processing. Engineer robust

prompts/tools: reusable templates, multi-turn strategies, structured outputs via JSON Schema/Pydantic. Select/tune models: foundation models, embeddings, rerankers; apply LoRA/PEFT or distillation when justified. Build eval corpora: golden sets, KPIs for accuracy, groundedness, deflection, tool success. Implement guardrails: PII/PHI detection, policy prompts, jailbreak resistance, filters, safety scorecards. Productionize: ship resilient services with analytics, alerts (drift, quality, cost), SLOs, etc. Optimize for scale: token, latency, cost; caching, context packing, batching, speculative decoding, routing by intent. What Will Help You Succeed Minimum Requirements Advanced CS/AI/ML degree or equivalent, strong ML background. 7+ years DS/ML, 3+ years NLP/LLMs, shipped production systems. Python and core ML stack: 5+ years of professional Python. Data engineering for unstructured data (3+ years): text processing, parsing, embedding-friendly preprocessing. Proven RAG expertise (1+ years): embeddings, retrieval, reranking, chunking. Evaluation depth (1+ years): offline/online evals for accuracy, grounding, safety. Safety/privacy (1+ years): moderation, PII/PHI redaction, policy enforcement. LLMOps (1+ years): prompt/version management, experiment tracking, monitoring. Excellent communication: explain trade-offs, drive data decisions. Desirable Experience Serving/scaling: vLLM/TGI, Ray Serve, Triton; GPU/CPU trade-offs. Tuning/distillation: LoRA/PEFT, safety alignment, synthetic data. Domain: observability, support systems, multilingual, regulated environments. Cloud/security: Snowflake, AWS, managed vs self-hosted. Experience with graph-based knowledge bases (e.g., GraphDB, Neo4j) and knowledge graphs to complement RAG systems with entity modeling and relationship-aware retrieval. Why you will love being a Dynatracer Working models that offer you the flexibility you need, ranging from full remote options to hybrid ones combining home and in-office work. A team that thinks outside the box, welcomes unconventional ideas, and pushes boundaries. An environment that fosters innovation enables creative collaboration and allows you to grow. A globally unique and tailor-made career development program recognizing your potential, promoting your strengths, and supporting you in achieving your career goals. A truly international mindset with Dynatracers from different countries and cultures all over the world, and English as the corporate language that connects us all. A culture that is being shaped by our global team's diverse personalities, expertise, and backgrounds. A relocation team that is eager

Dynatrace (NYSE: DT) is the leading AI-powered observability and security platform. We're advancing observability for today's digital businesses, helping transform modern digital ecosystems' complexity into powerful business assets.

Our AI-driven insights cut through the noise, allowing customers to focus on what truly matters by automating manual tasks and resolving issues with pinpoint accuracy. Dynatrace offers simplicity, clarity, and reliability at scale to ensure teams can make informed decisions, minimize downtime, and drive their business forward with confidence.