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Dynatrace

Lead Data Scientist (Remote EMEA wide)

Posted 8 Days Ago
Be an Early Applicant
Remote or Hybrid
Hiring Remotely in Barcelona, Cataluña
Senior level
Remote or Hybrid
Hiring Remotely in Barcelona, Cataluña
Senior level
The Lead Data Scientist will design and build generative AI capabilities using LLMs, overseeing the architecture, mentoring staff, and ensuring production readiness of the systems.
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Your role at Dynatrace

Dynatrace makes it easy and simple to monitor and run the most complex, hyper-scale multicloud
systems. Dynatrace is a full stack and completely automated monitoring solution that can trackevery user, every transaction, across every application.

Our team is looking for a Lead Data Scientist specialized in Large Language Models (LLMs) to design, build, and scale generative AI capabilities for real-world, enterprise-grade use cases. In this hands-on technical leadership role, you’ll own the end-to-end LLM stack, from data/knowledge, Ingestion and retrieval to prompt and tool-use architecture, evaluation frameworks,safety/guardrails, and cost/latency optimization.

Your Tasks

  • Own the LLM system architecture: Retrieval pipelines, prompt/tool design,
    routing/fallbacks, safety layers, and telemetry, optimized for quality, latency, and cost.
  • Establish technical standards for RAG: content ingestion, chunking/windowing, hybrid
    retrieval, reranking, query understanding, and 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 - staging - 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 experiance:

  • 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 to help you start your journey to a new country, always
    there to support and by your side. If you need to relocate for a position you’re applying for,
    we offer you a relocation allowance and support with your visa, work permit,accommodation .

Top Skills

AWS
Graphdb
Llms
Ml
Neo4J
Nlp
Python
Rag
Snowflake

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