Scientific Informatics Engineer

We're looking for a Scientific Informatics Engineer to join our Life Sciences practice in Gothenburg, Sweden in a hybrid working mode. In this entry-level role, you will work closely with EPAM's Life Sciences team to identify and develop opportunities where technology, data and process innovation can help a world-leading pharmaceutical organisation scale its early research capabilities. You will be embedded in an innovative life sciences environment, collaborating with scientists engaged in early-stage drug discovery and research. Projects will focus on understanding how data is generated across laboratory workflows-from sample management and assay execution to experimental data capture-and finding ways to connect, standardise and scale these processes through smart data integration. This position is ideal for someone eager to develop skills across technical, scientific and analytical dimensions. We don't expect you to be an expert in everything-you should have curiosity about life sciences and a desire to grow in data-driven roles that make a real impact on scientific research. Responsibilities Assist in analysing scientific and business problems to propose practical, data-driven solutions Gather, document and analyse business and technical requirements from multiple stakeholders Support development and maintenance of lab automation workflows and digital tools Map and analyse data flows within laboratory workflows and across connected scientific processes Identify integration gaps between data systems and workflows, supporting recommendations for improvement Collaborate with scientists, engineers and business teams to translate workflow needs into actionable steps Ideally, assist in defining data dictionaries and basic data models relevant to early research processes Document processes and support process-mapping to optimise efficiency in research environments Requirements Basic understanding of programming concepts; exposure to Python or similar scripting languages is a plus Familiarity with version control tools such as Git is advantageous Understanding of data structures, data management principles and analytical workflows Awareness of how data analytics and AI are applied in a scientific context Educational background or coursework in life sciences, biology, chemistry, biochemistry or related fields Basic familiarity with laboratory practices, equipment or safety protocols Understanding of early research workflows such as compound/sample management, assay execution or omics data generation Awareness of how scientific data is captured, stored and used in pharmaceutical or biotech research Familiarity with Electronic Lab Notebooks (ELNs) or Laboratory Information Management Systems (LIMS) is a plus Basic knowledge of data file formats relevant to life sciences (e.g., SDF, FASTA, CSV exports from instruments) Any prior hands-on lab experience (academic or internship) is advantageous Ability to engage with scientists, understand workflow pain points and translate them into structured technical requirements Comfort with process mapping and documenting task flows, including identifying manual steps Understanding that research environments demand high data quality and reproducibility along with efficiency Experience creating process flow visuals or performing root cause analysis is beneficial Nice to have Exposure to lab automation platforms or informatics tools Familiarity with bioinformatics file formats or analysis tools Understanding of Agile frameworks or experience in structured project environments Previous experience in business analysis, research assistance or project coordination roles