Responsibilities:
- Help develop an analytics platform that integrates insights from diverse data sources.
- Build, deploy, and test machine learning and classification models
- Train and retrain systems when necessary
- Design experiments, train and track performance of machine learning and AI models that meet specific business requirements
- ML and data labelling: Identify ways to gather and build training data with automated data labelling techniques and the creation of highly accurate training datasets.
- Automatic extraction of causal knowledge from diverse information sources such as databases, news, social media, videos and images etc.
- Develop customized machine learning solutions including data querying and knowledge extraction.
- Develop and implement approaches for extracting patterns and correlations from both internal and external data sources using time series machine learning models.
- Work in an Agile, collaborative environment, partnering with other scientists, engineers, consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviours.
- Distil insights from complex data, communicating findings to technical and non-technical audiences.
- [VK1] Develop, improve, or expand in-house computational pipelines, algorithms, models, and services used in crop product development.
- Constantly document and communicate results of research on data mining, analysis, and modelling approaches.
Must have:
- 5+ years of experience working with NLP and ML technologies.
- Proven experience as a Machine Learning Engineer or similar role.
- Experience with NLP/ML frameworks and libraries
- Proficient with Python scripting language
- Background in machine learning frameworks like TensorFlow, PyTorch, Scikit Learn, etc.
- Demonstrated experience developing and executing machine learning, deep learning, data mining and classification models.Conversant with the latest in NLP and NLU models including transformer architectures and in creating explainable AI
- Ability to communicate the advantages and disadvantages of choosing specific models to various stakeholders
- Proficient with relational databases and SQL. Ability to write efficient queries and optimize the storage and retrieval of data within the database. Experience with creating and working on APIs, Serverless architectures and Containers.
- Creative, innovative, and strategic thinking; willingness to be bold and take risks on new ideas.
- Implementation expertise in ML models and specialized experience in NLP.
Great to have Qualifications:
- Experience in Social data analysis like Twitter, Facebook, Instagram, Amazon reviews
- Experience w: AWS, Docker, Git, Jupyter, APIs, Azure.
Previous experience in NLP projects to extract behaviours, intentions, trends, correlations and relevant insights from text and social data.
[VK1]Already mentioned in point 3