Data Scientist - Enterprise Search

Posted 12 Days Ago
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Madrid, Comunidad de Madrid, ESP
In-Office
Mid level
Healthtech • Biotech • Pharmaceutical
The Role
Develop enterprise search and information retrieval solutions using generative AI, LLMs, RAG, agentic architectures, machine learning, and vector databases. Responsibilities include model development and evaluation, prompt engineering, search relevance tuning, data pipelines, embeddings, MLOps, API integration, experimentation, and stakeholder consulting. The role also leads proof-of-value projects, supports data-driven decisions, and improves scalable search systems.
Summary Generated by Built In

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

The Data Scientist - Enterprise Search role is responsible for contributing to the design and  development of the next-generation Enterprise Search and information retrieval architectures.

This role will build context-aware, generative AI-driven search systems optimized for agentic readiness—enabling autonomous tool orchestration, deep semantic understanding, and intelligent information retrieval at an enterprise scale.

The data scientist will develop advanced AI solutions, with a strong focus on Generative AI, LLM-based applications, and scalable data services. This requires to work with large datasets, develop and evaluate machine learning models, and collaborate with cross-functional teams to improve the accuracy, coverage, relevance, and performance of search algorithms at enterprise scale.  

This role involves direct communication with project stakeholders and contributes to team best practices, while identifying optimization opportunities that enhance the impact of moderately complex data solutions within larger product architectures. You will leverage advanced technical skills to translate business needs into actionable data science initiatives.

Job Responsibilities

Generative AI, Agentic AI and LLM Optimization

  • Model Development & Experimentation: Lead exploratory data analysis, feature engineering, model selection, training, validation, and performance evaluation for machine learning and AI-enabled solutions.  Design and evaluate multiple modeling approaches, establish appropriate evaluation metrics, and optimize models for scalability, reliability, and business impact. 

  • Experimentation and Innovation: lead experimental projects and drive innovation in enterprise search, exploring novel approaches like GraphRAG or agentic search patterns. 

  • Agentic AI Search: Develop and deploy intelligent agentic architectures that can interact with and enhance the enterprise search experience.

  • RAG Experimentations (RAG Evaluation Framework): Design and conduct Retrieval-Augmented Generation experiments to evaluate and improve search relevance and performance.

  • LLM Model Evaluation: Evaluate the performance of Large Language Models in various enterprise search contexts, ensuring they meet business requirements and performance standards.

  • Advanced Prompt Engineering: Design and optimize prompts to programmatically enhance the interaction and effectiveness of search queries and responses.

Business Problem Solving & Decision Support 

  • Partner closely with business stakeholders and product teams to translate complex business challenges into analytical approaches, ML solutions, and scalable intelligence capabilities. 

  • Support data-driven prioritization and strategic decision making through actionable insights, predictive models, and operational intelligence. 

  • Conducts A/B testing and experiments to assess the performance of search models and algorithms.

  • Consultancy Provide expert consultancy on data science and machine learning best practices, guiding internal teams and stakeholders.

  • PoC and knowledge sharing: Design and lead proof-of-value (PoV) projects, conduct knowledge-sharing sessions, and deliver impactful demos to showcase capabilities and gather feedback.

Data Engineering & Vector Databases

  • Data Engineering & Processing: Work with structured and unstructured data, building efficient pipelines for data ingestion, preprocessing, and feature engineering.

  • Vector database experimentation: Conduct experiments with vector databases to improve the efficiency and accuracy of our search systems.

  • Managing embeddings: Implement and optimize techniques for embeddings generation, indexation and retrieval to support advanced search queries and retrieval capabilities.

  • Retrieval & relevance tuning: Develop, evaluate, and tune retrieval algorithms to optimize search precision, recall, and relevance for diverse datasets.

  • Data quality: Design data enhancement modules that extract, enrich, and validate document content and metadata, directly improving downstream model context, search recall, and agentic reasoning.

Model Lifecycle and integration

  • Deployment, Testing, and Training of ML Models and Endpoints: Develop, deploy, and continuously refine machine learning models and endpoints to enhance search functionalities. Conduct rigorous testing and validation to ensure model accuracy and reliability.

  • Development of ML Models for search: Create and deploy advanced runnable models, such as entity extraction models and metadata augmentation, to expand the capabilities of our search solutions.

  • MLOps & Monitoring: Implement best practices for model deployment, versioning, monitoring, and performance optimization.

  • API & MCP Interoperability: Interface with enterprise search engines, platforms, and other APIs to enhance our search functionalities and integrations. Familiarity with MCP and protocols for agent interoperability.

Qualifications

Education / Experience

  • Master’s degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a related quantitative field. 

  • Demonstrated experience as a rising expert developing predictive models and leading specific analytical modules or project components.

  • Proven track record of taking full accountability for the quality and timely delivery of analytical tasks and troubleshooting complex data issues independently.

  • Experience working effectively on moderately complex data science problems and understanding how contributions fit into medium-sized data architectures.

Technical Skills

  • Shows strong proficiency in programming languages, particularly Python.

  • Has proven experience as a Data Scientist, preferably with a focus on information retrieval and NLP.

  • Has a solid understanding of natural language processing (NLP) techniques and tools.

  • Possesses hands-on experience with machine learning and deep learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).

  • Familiarity with cloud platforms and services, particularly AWS or Microsoft Azure.

  • Familiarity with version control systems (e.g., Git) and agile development practices.

  • Past experience with search engines and technologies (e.g. Elasticsearch, Solr, or Lucene)  and solid understanding of search algorithms, information retrieval, and relevancy tuning is a plus.

  • Demonstrates excellent analytical and problem-solving skills, with the ability to work with large, complex datasets.

  • Proven ability to translate well-defined business questions into clear analytical problems and technical solutions.

Additional Qualifications

  • Strong communication and collaboration skills, with the ability to manage direct communication with immediate project stakeholders.

  • Ability to actively integrate feedback from technical peers and junior team members.

  • Proactive mindset to identify potential optimizations or new analytical approaches within the project scope.

  • Ability to work autonomously to achieve goals and deliver results, while actively collaborating with team members to meet shared team objectives. 

  • Experience in healthcare, pharmaceutical, or other regulated industries is a plus.

******40% monthly time working from the office is required ****
 

 

 

Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.


Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.

Skills Required

  • Master's degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a related quantitative field
  • Demonstrated experience developing predictive models and leading analytical modules or project components
  • Experience taking accountability for analytical task quality and timely delivery
  • Experience troubleshooting complex data issues independently
  • Experience working on moderately complex data science problems and understanding medium-sized data architectures
  • Strong proficiency in Python
  • Experience as a Data Scientist, preferably focused on information retrieval and NLP
  • Understanding of natural language processing techniques and tools
  • Hands-on experience with machine learning and deep learning frameworks such as TensorFlow, PyTorch, or scikit-learn
  • Familiarity with AWS or Microsoft Azure
  • Familiarity with Git and agile development practices
  • Experience with search engines such as Elasticsearch, Solr, or Lucene and search relevance tuning
  • Strong analytical and problem-solving skills with large, complex datasets
  • Ability to translate business questions into analytical problems and technical solutions
  • Strong communication and collaboration skills
  • Ability to integrate feedback from technical peers and junior team members
  • Ability to work autonomously while collaborating with team members
  • Experience in healthcare, pharmaceutical, or other regulated industries

Roche Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Roche and has not been reviewed or approved by Roche.

  • Retirement Support U.S. materials describe a 401(k) with both matching and an additional company contribution, supported by formal plan documents and true‑up features. This structure is positioned as a standout element of the total package, particularly at Genentech.
  • Leave & Time Off Breadth Time‑off provisions include substantial vacation, a year‑end shutdown, and a paid six‑week sabbatical after six years. These elements indicate a recharge‑oriented approach within the U.S. offering.
  • Healthcare Strength Company materials emphasize comprehensive medical, dental, vision, and mental‑health resources alongside well‑being programs. Benefits pages consistently highlight breadth across core health coverage elements.

Roche Insights

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The Company
Provincia de Buenos Aires
93,797 Employees
Year Founded: 1896

What We Do

Roche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people’s lives. The combined strengths of pharmaceuticals and diagnostics under one roof have made Roche the leader in personalised healthcare – a strategy that aims to fit the right treatment to each patient in the best way possible. Roche is the world’s largest biotech company, with truly differentiated medicines in oncology, immunology, infectious diseases, ophthalmology and diseases of the central nervous system. Roche is also the world leader in in vitro diagnostics and tissue-based cancer diagnostics, and a frontrunner in diabetes management. Founded in 1896, Roche continues to search for better ways to prevent, diagnose and treat diseases and make a sustainable contribution to society. The company also aims to improve patient access to medical innovations by working with all relevant stakeholders. Thirty medicines developed by Roche are included in the World Health Organization Model Lists of Essential Medicines, among them life-saving antibiotics, antimalarials and cancer medicines. Roche has been recognised as the Group Leader in sustainability within the Pharmaceuticals, Biotechnology & Life Sciences Industry ten years in a row by the Dow Jones Sustainability Indices (DJSI).

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