Data Scientist

About the Position

We are seeking a Data Scientist to join our AI incubator projects, developing advanced AI solutions and technology roadmaps. This role involves participating in projects to scout, incubate, and validate client and PwC-internal ideas over a 3–5 year horizon.

Responsibilities

• Graph AI, Knowledge Graphs & Graph Data Science

  • Design and implement graph-based solutions, including graph features, graph analytics, and graph ML workflows aligned to business use cases.
  • Build and operate knowledge graphs (schema/ontology, entity resolution, ingestion/update pipelines) in graph databases (e.g., Neo4j) and apply graph representation learning (embeddings; GNNs such as GCN/GAT) to support prediction, ranking, and reasoning.

• NLP, LLMs, Semantic Engineering & RAG

  • Build LLM-powered capabilities end-to-end: prompt/tool design, semantic engineering, grounding strategies, and production-grade RAG pipelines (chunking, embeddings, reranking, citations/attribution where applicable).
  • Deliver NLP features such as text-to-SQL/semantic parsing, classification/extraction, and domain adapters; incorporate user/context signals to enable search, retrieval, and personalization across structured and unstructured sources.

• Core Data Science, Machine Learning & Deep Learning (Core Requirement)

  • Formulate problems, define success metrics, and design experiments; select and train ML/DL models appropriate for the task, data shape, and operational constraints.
  • Perform rigorous evaluation and error analysis (offline metrics, slice analysis, ablations), and iterate on features, architectures, and data to improve performance and robustness.

• Software Engineering, Architecture & Workflow Design

  • Design end-to-end solution architectures and workflows that connect data sources, graph layers, retrieval, models, and application surfaces into coherent, maintainable systems.
  • Implement production services and APIs (batch + real-time) that expose model, retrieval, and graph capabilities with clear contracts, security considerations, and performance targets.

• Agile Delivery, Quality, Observability & E2E Ownership

  • Own delivery in agile pods: break down problems, estimate, ship increments each sprint, and continuously improve based on feedback and measured outcomes.
  • Build quality into the workflow: automated tests, evaluation suites for ML/LLM behavior, CI checks, code reviews, and clear operational runbooks.

• Compute, Performance & Scaling (Preferred)

  • Optimize training and inference pipelines for performance and cost, including efficient batching, caching, quantization/acceleration approaches where appropriate, and practical profiling.
  • Leverage GPUs and accelerated compute for deep learning/LLM workloads; understand memory constraints, throughput bottlenecks, and practical optimization trade-offs.

Requirements

• Core Skills

  • Python: Strong Python engineering experience (3–6+ years).
  • Graph Data Science & Knowledge Graphs: Experience with graph data science and/or knowledge graphs (modeling, ingestion, graph features/embeddings; experience with graph databases such as Neo4j); experience with GNNs (e.g., GCN/GAT) is a plus.
  • NLP, LLMs & Semantic Engineering: Familiarity with modern NLP and language systems; semantic engineering; search/retrieval (lexical + vector + hybrid) and reranking; RAG; personalization; text-to-SQL; prompt engineering; fine-tuning/adaptation patterns.
  • Core ML / Deep Learning / Data Science: Strong grounding in machine learning and deep learning; applied data science; ability to design experiments, evaluate models, and reason about quality, robustness, and bias.
  • Software Engineering & Delivery: Solid, hands-on development experience (production Python and/or related stack); agile ways of working; R&D prototyping; architecture and workflow understanding and design across services, APIs, and data pipelines.
  • APIs, Microservices, and Cloud Platforms: Solid knowledge of APIs, microservices, and data-centric integrations. Comfort with cloud platforms (Azure, AWS, GCP) and CI/CD workflows.
  • Compute & Scale: Preferred: exposure to GPUs, accelerated training/inference, and/or distributed compute (e.g., Ray, Spark, Dask, or similar patterns).

• Foundational Engineering Skills

  • Excellent debugging, profiling, and performance optimization skills.
  • Strong understanding of source control (Git), branching, PR reviews.
  • Ability to work in structured agile environments with clear sprint increments.

Mindset

• High ownership, curiosity, and willingness to experiment.
• Loves hard problems and iterating on prototypes.
• Comfortable working in fast-changing frontier environments.
• Values clarity, structure, and quality in code.

We Offer

• Fully remote position with occasional in-person team sessions/workshops/gatherings in Prague.
• Minimum 2-6pm CET overlap with preferred 2-7pm CET.

About the Company

We are committed to fostering a diverse and inclusive workplace. Join us to be part of a team that values innovation, collaboration, and excellence.

Apply for this Position

Our team will review your application within the next 5 days.

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