Job Description
Senior Machine Learning Research Scientist
Who we are :
Sage Artificial Intelligence Labs "SAIL" is a nimble team within Sage building the future of cloud business management by using artificial intelligence to turbocharge our users' productivity.
The SAIL team builds capabilities to help businesses make better decisions through data-powered insights.
As a part of our team, you will be crafting machine learning solutions to help steer the direction of the entire company’s Data Science and Machine Learning effort.
You will have chances to innovate, contribute and make an impact on the rapidly growing FinTech industry.
As part of our research team, you will help define applied AI / ML research within Sage. You will work on hard, open-ended research problems, create novel solutions, build proofs of concept, and present your findings and results all with an eye towards applying emerging research to real world ML problems.
If you share our excitement for machine learning, value a culture of continuous improvement and learning and are excited about working with cutting-edge technologies, apply today!
You might work on
Investigating cutting-edge AI / ML data privacy techniques for financial applications
Contributing to writing peer-reviewed scientific publications and patent applications
Building, experimenting, training, tuning, and shipping machine learning models and prototypes
Contributing to the wider research community by sharing and publishing your findings, with other teams at Sage as well as with collaborations from other research programs
Define and develop metrics and KPIs to identify and track success
Working collaboratively with cross-functional members in SAIL to discover innovative research directions that will have a large impact on our ML Products and Strategy
Take an active role within the team to contribute to its objectives and key results (OKRs) and to the wider AI strategy
Adopt a pragmatic and innovative approach in a lean, agile environment
Presenting findings, results, and performance metrics to stakeholders
Technical / professional qualifications
Deep understanding of statistical and machine learning foundations
Excellent analytical, quantitative, problem-solving and critical thinking skills
Ability to understand from first-principles the entire lifecycle : training, validation, inference, etc.
Experience designing, developing and scaling machine learning models in production
Ability to assess and translate a loosely defined business problem with emerging ideas and techniques from the research community
Strong technical leadership with the ability to see project initiatives through to completion
3+ years experience in doing applied research and development
A track record of publishing your findings and presenting your work to the wider community
Proficiency with Python, R, Pandas and ML frameworks such as scikit-learn, PyTorch, TensorFlow etc
MS in Computer Science, Mathematics, Economics, Statistics, Physics, or similar quantitative field
Strong theoretical and mathematical foundations in linear algebra, probability theory, multivariate optimization
Have a strong intuition into different modeling techniques and their suitability to different problems
Experience communicating complex, technical ideas to both technical and non-technical audiences
Preferred Qualifications :
PhD in Computer Science, Mathematics, Economics, Statistics, Physics, or similar quantitative fields
Experience wrangling data, writing SQL queries and basic scripting
Deep experience with one or more technical areas : convex optimization, gradient descent, regularization, cross-validation, overfitting, bias, variance, numerical methods in linear algebra, sampling, latency, computational complexity, sparse matrices, deep learning, reinforcement learning
You may be a fit for this role if you :
You’re comfortable investigating open-ended problems and coming up with concrete approaches to solve them.
You know the right balance of exploration and exploitation when pursuing a new program of research
You don't only use machine learning models or libraries but can implement many machine learning and statistical learning models from scratch and know when / how to apply them to real world noisy data
You’re a deeply curious person and eager to learn and grow
You often think about applications of machine learning in your personal life
What's it like to work here
You will have an opportunity to work in an environment where Data Science is central to what we do. The products we build are breaking new ground, and we have a focus on providing the best environment to allow you to do what you do best - explore emerging technologies, solve novel problems, and collaborate with their wider team to apply your solutions to solve real customer problems.
Our distributed team is spread across multiple continents, we promote an open diverse environment, encourage contributions to open-source software and invest heavily in our staff.
Our team is talented, capable and inclusive. We know that great things can only be done with great teams and look forward to continuing this direction.
Key ResponsibilitiesYou might work on
Investigating cutting-edge AI / ML data privacy techniques for financial applications
Contributing to writing peer-reviewed scientific publications and patent applications
Building, experimenting, training, tuning, and shipping machine learning models and prototypes
Contributing to the wider research community by sharing and publishing your findings, with other teams at Sage as well as with collaborations from other research programs
Define and develop metrics and KPIs to identify and track success
Working collaboratively with cross-functional members in SAIL to discover innovative research directions that will have a large impact on our ML Products and Strategy
Take an active role within the team to contribute to its objectives and key results (OKRs) and to the wider AI strategy
Adopt a pragmatic and innovative approach in a lean, agile environment
Presenting findings, results, and performance metrics to stakeholders
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