Job Description
Are you interested in working with data and analytics to solve problems? Are you interested in bringing and building up your NLP and (gen) AI expertise to projects? About our Team We are a diverse team of natural language processing and gen AI experts, taxonomy experts and scientific content experts in biology and chemistry domains. We mainly develop best-in-class enrichment algorithms that deeply mine scientific literature (journals and patents) for Elsevier life science .com products such as Reaxys and Embase. About the Role You will be responsible for building, testing and maintaining our NLP solutions. You will work throughout the whole life cycle of data science projects: design, implementation, productionisation and beyond. You will deliver efficient and production ready Python code. You will collaborate with the technology team to deploy and productionize our data science pipelines.ResponsibilitiesData collection, data analysis, model development, defining quality metrics, quality assessment of models and regular presentations to stakeholdersCreating production ready Python packages for each component of data science pipelines (such as pre-processing and model inference) and their deployment together with the technology teamIntegration of data science components and end-to-end quality assessmentKeeping our data science pipelines robust against model drift and ensuring continuous output quality; development of needed tools and strategies for maintenance such as automatic model re-training.Establishing the reporting process of the performance of the pipeline, and automatic re-training strategy for the existing pipelinesRequirementsAt least 2 years of relevant applied experience or Msc/MTech in the field of computer science, data science, artificial intelligence, mathematics, statistics, bioinformatics or other quantitative fields with at least 1 years of relevant experience. International working/education experience is a plus!Strong hands-on knowledge of Python, ability to write unit tests and production ready code adhering to Python best practices and object oriented programming principles.Data processing, cleaning and analysis skills: experience with pandas, numpy, matplotlib, boto3Experience with SOTA deep learning approaches in NLP domain such as LLMs and finetuning for specific use cases such as named entity recognition and relation extractionAffinity with gen AI solutions, various LL models, vectorization methodologies and evaluation of LLMsExperience with CI/CD, Git, PyTorch, AWS services such as SageMaker. Experience with Spark/Databricks is a plus!Willingness to learn, analytical thinking, problem solving and communication skills; ability to translate complex requirements into practical solutionsExperience in classical machine Learning: Classification, Regression, Clustering, Text Mining. You have an excellent understanding of Neural Networks, Random Forests, Logistic Regression, SVM, K-Means etc.Experience in later stages of data science life cycle such as optimizing productionization (techniques such as parallelization, multi threading etc.) and automated model re-training. Interest and affinity in MLOps is a plus!-----------------------------------------------------------------------Elsevier is an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form: https://forms.office.com/r/eVgFxjLmAK , or please contact 1-855-833-5120.Please read our Candidate Privacy Policy.
