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.
You will have overall technical ownership of designing, developing, delivering, and maintaining high quality machine learning solutions that contribute to the success of Sage and contributes intelligence to its products.
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!
This is a hybrid role – three days per week in our Newcastle office.
- Building, experimenting, training, tuning, and shipping machine learning models in the areas of: classification, clustering, time-series modelling and forecasting.
- Define and develop metrics and KPIs to identify and track success
- Working with product managers and engineers to translate product/business problems into tractable machine learning problems and drive the ideas into production using machine learning
- Collaborate with architects and engineers to deliver ML solution and ship code to production
- 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 and advise on the best approaches to deliver quality Machine Learning solutions
- Strong technical leadership with the ability to see project initiatives through to completion
- Extensive industry experience training and shipping production machine learning models.
- Proficiency with Python, R, Pandas and ML frameworks such as scikit-learn, PyTorch, TensorFlow etc
- MS in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative field.
- Strong theoretical and mathematical foundations in linear algebra, probability theory, multivariate optimization.
- Have a strong intuition into different modelling techniques and their suitability to different problems.
- Experience communicating projects to both technical and non-technical audiences.
Preferred Qualifications:
- PhD in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative fields.
- Experience with NLP and applying ML in the Accounting/Finance domain a plus
- Experience wrangling data, writing SQL queries and basic scripting.
- Deep experience with: logistic regression, gradient descent, regularization, cross-validation, overfitting, bias, variance, eigenvectors, sampling, latency, computational complexity, sparse matrices.
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 don\'t only use machine learning models 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 - solve problems, collaborate with your team and push first class software. 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.
