Senior Machine Learning Research Scientist
Sage Intacct
San Jose
hace 5 días

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

LI-LH1 #LI-Remote

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