Senior Machine Learning Research Scientist

Sage

San Jose

hace 1 día

People make Sage great. From our colleagues delivering ground-breaking solutions to the customers who use them : people have helped us grow for more than thirty years, and people are driving our future as a great SaaS company.

We're writing our next chapter. Be part of it!

At Sage, we recognize that the world of work has rapidly shifted over the last few years, particularly how we work. That is why we have committed to working in a hybrid way going forward.

Human connection is an essential ingredient of the 4 principles that make up our Flexible Human Work hybrid framework and we want to be transparent in what that looks like when you join our Sage family.

On one hand, our offices will continue to play an important role in our future and serve as a place for spontaneous conversations, connection, collaboration as well as focused time.

On the other hand, we have learned to reimagine where and when we work and to unlock that flexibility and innovation for our colleagues offering them the opportunity to work flex across their home, Sage offices or customer sites.

We invite you to join us and help us write our next chapter. Follow us on our social media sites to join in conversations about open positions and company news! #lifeatsage #sagecareers.

If you would like support with your application (or require any adjustments) please contact us at for assistance. All qualified applicants will be thoughtfully considered and never discriminated against based on their race, color, age, religion, sexual orientation, gender identity, national origin, disability or veteran status.

EOE AA / M / F / Vet / Disability Sage Software is an Equal Opportunity Employer. We comply with the laws set forth in the Equal Employment Opportunity in The Law poster :

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!

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.

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

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