The Pfizer Machine Learning and Intelligent Assistance Team, builds, delivers and maintains the products, platforms and capabilities that en...
The Pfizer Machine Learning and Intelligent Assistance Team, builds, delivers and maintains the products, platforms and capabilities that enable Pfizer Business Units to leverage intelligent automation solutions to effectively engage/collaborate with teams internally and our customers externally. We build advanced AI technology solutions at a global scale that positively impact Pfizer business performance. Pfizer is accelerating the use of AI technologies across our entire enterprise.
We are looking for a natural language processing (NLP) engineer to join the Intelligent Solutions Team to scale and help us improve our NLP products and create new efficient self-learning NLP applications and products.
You will need to apply state of the art NLP and ML techniques, build robust pipelines to enable end to end intelligent solutions for unique business problems. You will be responsible for developing the NLP vision for new projects and will need a keen understanding of NLP/ML models, solutions and technologies. Finally, you will also assist in evangelizing AI/ML/NLP across Pfizer and promote adoption and enablement of NLP solutions across Pfizer by providing education, guidance and mentorship to junior members of the team and the broader Pfizer digital and business community.
The ideal candidate will be passionate about health care, software engineering, machine learning, NLP and stay up-to-date with the latest developments in the field.
- Understand business problems/objectives and develop strategy/roadmap to pilot and build NLP based software products that help to achieve business goals for business clients
- Lead the development of robust NLP and machine learning pipelines to deliver stable and efficient production-ready components and solutions.
- Articulate experiment plans that demonstrate the process of model building, refinement and productization
- Supervise the data acquisition process if more data is needed
- Find available datasets online that could be used for training
- Define validation strategies
- Define the preprocessing or feature engineering to be done on a given dataset
- Define data augmentation strategies
- Train models and tuning their hyperparameters
- Analyze the errors of the model and design strategies to overcome them
- Deploy models to production
- Present model outcomes in a scientifically rigorous manner
- Produce executive reports and visualizations for decision makers throughout the organization
- Directly engage with key business stake-holders (Director/Sr. Director level)
- Lead projects with vendor resources and business stakeholders from requirements gathering through the full software development life cycle.
- Shared-ownership of advancing team's capabilities in Machine Learning and NLP.
- Managerial responsibility for up to five interns and contingent workers
- Masters degree in Data Science, Computer Science, Informatics, life sciences, physics, applied mathematics, statistics or related field
- 5 years as a data scientist, Machine Learning or NLP engineer
- 5 years working with different types of enterprise and real world data sets - structured, semi-structured and unstructured data
- 4 years building ML/NLP based software solutions
- Deep understanding and workings of contextual search products and solutions
- Strong understand of Software Engineering and Development Life Cycle principles
- Expertise in supervised and unsupervised Machine Learning techniques, with emphasis on Natural Language Processing (NLP)
- Experience building production-ready NLP systems, from preprocessing and normalization to monitoring model drift in a production environment, ideally using NLP libraries and technologies including Spacy, PyTorch & Deep Learning models
- Proficiency in leveraging cloud-based machine learning resources such as those from Amazon Web Services or Google Cloud for model training and productization
- Expertise in NLP feature engineering and modeling (e.g., text classification, entity recognition, dependency parsing)
- Experience with SoTA modeling techniques, such as transformers (e.g., BERT, GPT-_N_),
- Experience in applying NLP in multi-lingual and multi-modal contexts
- Experience taking an NLP project from concept to production
- Expertise with Python
- Building deep neural networks with modern tools, such as PyTorch or Tensorflow
- Building, testing, and deploying computer vision based solutions
- Writing unit tests
- Collaborating via Git
- Ability to thrive in a fast-paced multi-disciplinary environment; with the ability to effectively communicate with a diverse audience
- Experience with hyperparameter optimization, model selection and validation.
- Experience with implementation of solutions with DevOps tools within the CI/CD pipeline (eg. Docker, Kubernetes)
- Good proficiency with SQL, Python, Scala, or Java as well as formal statistical tools R, SAS, etc.
- Excellent written and verbal communication skills
Strong Analytical Thinking and Problem Solver.
NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS
Flexibility to work across multiple time zones (i.e.: EST, PST, GMT, IST)
Last Date to Apply for Job: 8/17/2021
Eligible for Employee Referral BonusSunshine Act
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