This is an exciting opportunity to support pharmaceutical and biotech companies deploying innovative scientific R&D technologies globally. You will enable R&D Digital Transformation, design efficient data pipelines using the latest in Big Data Cloud Infrastructure and engineer new data analytics/visualization capabilities. You will empower our clients to build their future data landscape for data-driven decisions.
Requirements- Build ingestion pipelines for clinical trial protocols, ICF documents, SmPCs, CSRs, and published articles (PubMed, CTIS, ClinicalTrials.gov) - handling PDF parsing, text extraction, and structured data normalization
- Design and implement data models in Amazon Aurora (relational) and GraphDB (knowledge graph) to represent trial design entities: endpoints, eligibility criteria, study arms, interventions, therapeutic areas, and their relationships
- Develop embedding and vectorization pipelines to prepare extracted clinical text for RAG-based retrieval in LangGraph agentic workflows - chunking strategies, metadata enrichment, and vector store population
- Build and maintain ETL/ELT workflows that transform unstructured clinical content into queryable, linked data across both relational and graph stores
- Implement data quality validation specific to clinical data - protocol section classification accuracy, entity extraction completeness, cross-reference integrity (NCT IDs, EudraCT numbers, MeSH terms)
- Build data serving APIs (Python/FastAPI) that expose curated datasets to the Angular frontend and LangGraph agent layer
- Set up data lineage tracking and audit trails to support regulatory traceability of AI-generated trial design recommendation
Required SkillsResponsibilities:
- Identify and analyze business needs to determine data related requirements,
- Clean, transform, and organize big data for analysis,
- Conduct data analysis to identify trends, patterns, and insights,
- Develop data visualization tools to communicate insights,
- Provide consulting inputs for guidelines, design ideas, standards, & best practices,
- Help stakeholders to estimate, plan, and prioritize data engineering efforts,
- Build relationship with clients, internal and partners' leadership team, and
Required Skills:- Expertise in Python, Spark, Java, Scala for building data pipelines, warehouses, and data processing systems,
- Expertise in relational databases like SQL & NoSQL,
- Strong understanding of NoSQL databases like MongoDB, Cassandra, and HBase,
- Familiarity with data standards such as FAIR, JSON, OWL and markup languages such YAML
- Understanding of AI/ML, data science concepts & cloud architecture
Qualifications:- Bachelor's or Master's degree in data engineering / computer science or related field
- Experience with scientific informatics solutions OR background in biological science, chemistry, health sciences OR an experience working in the life or health science industry,
- Experience in cloud computing platforms like AWS, Azure, and Google Cloud Platform (GCP) for deploying and managing data infrastructure in the cloud.
- Experience using version control systems like Git for collaborative code development,
- Experience in data focused solutions such as Snowflake, Databricks, Kafka, Cloudera, Knime, Airflow, etc.
- Experience with AI/ML & data science libraries like Scikit-Learn, TensorFlow, PyTorch, HuggingFace, etc. for preparing data for downstream AI/ML applications,
- Familiarity with data visualization solution like Tableau, Power Bi and D3.js,
- Data engineering related certification from cloud platform like Azure/AWS/Cloudera, and
- Customer centricity to deliver at the highest standards and elevate customer experience.
- Expereince with Clinical Data is a plus.
BenefitsWe offer a competitive compensation package including accrued vacation, medical, dental, vision, 401k with company matching, life insurance, and flexible spending accounts.
If you share these sentiments and are prepared for the atypical, then Zifo is your calling!
