We are looking for a passionate, talented, and inventive machine learning scientist, with a strong background in machine/deep learning research and development, to support us in our mission of building industry-leading peer-group identification technology. Our mission is to push the envelope in company analysis technology to provide our users with the best possible analytical experience.
As an ML scientist, you will leverage heterogeneous data sources and large-scale computer resources and work with our world-class team to develop novel algorithms and modelling techniques to advance our state-of-the-art technology in company analysis.
This role requires pragmatic technical leadership, comfort with ambiguity and capabilities to summarize complex data and models through clear visual and written explanations.
The ideal candidate will have experience with machine learning models and their application in AI-based products. We are particularly interested in your experiences with applying natural language processing, graph learning, deep learning, and reinforcement learning at scale.
Additionally, we seek candidates with strong rigor in research and development, creativity, curiosity, and excellent judgment.
What you will be doing
- Develop machine learning models to build our peer-group analysis technology for finance professionals
- Drive the Comparables.ai ML technology in close collaboration with other machine learning scientists, software engineers and product managers
- Initiate and define new research and development ideas to solve the peer-group analysis problem, drive execution, and align across the teams and collaborators
- Work with the tech stack of Python/Tensorflow/Keras/Pytorch/Jupyter notebooks
- Write reusable, testable, and efficient code
- Publish scientific research in top conferences and journals
What we are looking for
- PhD degree with a proven track record of research experience in Machine Learning or a Masters degree and 4+ years of experience of applied research in Machine Learning
- Comprehensive and deep knowledge in fields such as machine learning/statistical modelling, deep learning, NLP, NLU, graph learning or reinforcement learning/user intent modelling with at least one publication as the first author in a leading conference or journal related to one of these fields
- Computer Science fundamentals in algorithm design, complexity analysis, data structures, problem-solving and diagnosis
- Experience applying machine learning research in information retrieval, search, information discovery or recommender systems
- Solid experience with programming languages such as Python
- Good written and spoken English communication skills and the ability to present complex solutions with clarity
- Flexible and responsive to spontaneous needs, challenges and opportunities, able to balance conflicting demands on time and priorities
- Highly self-organized, planned and collaborative with a strong personal drive to keep up with a productive mindset
- Continual passion to learn and apply knowledge in a methodical fashion
You might also have
- Experience in probabilistic modelling and Bayesian Inference
- Hands-on experience implementing machine learning systems at scale in Python or similar languages and a variety of libraries like Tensorflow, Keras, Pytorch, Numpy, Pandas and Scikit-learn
- Knowledge of MLOps and understanding of fundamental design principles behind a scalable application
- Familiarity with well-known cloud computing and storage service platforms such as AWS
- Good coding skills and engineering practices with agile software process and research-driven development
- Knowledge of DevOps methodologies and project management tools such as Git, Jira and Confluence
- To support the development and deployment of models in production
- To work closely with our in-house developers/engineers to supply them with vital tools
How to apply
If you are interested in this role and you would like to learn more, please contact Muhammad Ammad-ud-din at email@example.com or send your resume/CV directly to