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Comfortable with investigating open-ended problems? Enjoy challenging work with a diverse, highly capable engineering team?
As a data scientist in AutoEntry you will be tasked with improving our data extraction capability, there are many angles from where the problem domain can be approached which will eventually lead to a non-trivial system of many models acting in unison to extract data from every document we receive. Improving our data extraction capability is a fundamental foundation on which we will build and scale data capture globally within Sage.
AutoEntry build innovative cloud products and services and deal with lots of data. This gives our Engineers the opportunity to work on award winning software that will be shipped regularly, is used by a large number of people, requires an implementation that scales, requires best practice security measures and must be reliable. We receive well over a million invoices and receipts each month, with a volume that is rapidly growing. Our in-house developed ML platform combines this high volume of data with human annotation, which provides our data scientists with a lot of annotated data for research and allows us to quickly develop and evaluate a variety of ML models.
We are expecting a fast-paced adoption of new ML approaches in the near future, improving accuracy and embracing new challenges in other related problem domains. Our cloud-based architecture employs microservices to give modularity, which allows us to quickly transfer new predictive models to production in short R&D cycles.
Sage acquired AutoEntry in 2019 - demonstrating our commitment to innovation and adding value to Sage Business Cloud Accounting and Financial Management solutions. AutoEntry is one of the fastest growing automation software businesses in the market. Its intelligent technology eliminates the pain point of data entry for accountants, bookkeepers and businesses, so they can spend time on the things that really matter to their business.
Key accountabilities and decision ownership:Solving problems from ideation to production, using machine learning.
Experimenting, training, tuning, and shipping machine learning models.
Writing production-quality code.
Exploratory data analyses and investigations.
Working with product managers to translate product/business problems into tractable machine learning problems.
Working with machine learning infrastructure engineers to ship models.
Presenting findings, results, and performance metrics to a broad range of stakeholders, including senior management.
Influencing the broader development of the data science discipline within the organisation
Be the subject matter expert demonstrating mastery of the delivery and use of Data Engineering techniques and Science and its supporting technologies
Empower internal stakeholders using the art of the possible and to gain new meaning from data
Strong background in Data Science and Machine Learning
A degree in Math, Statistics, Engineering, Computer Science or related field is expected; advanced degrees are preferred.
A strong understanding of machine learning theory; experience in various aspects of ML such as classification, feature engineering, clustering, supervised learning, model selection.
Problem solving and analytical skills.
Hands-on experience applying ML techniques to solve real-word problems.
Ability to analyze and manipulate complex, high-volume, high-dimensional data from different sources.
Experience with common data science toolkits, such as R, Weka, scikit-learn, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable.
Previous experience in research projects and publication of scientific papers is a plus.
Experience in Python, scikit-learn, pandas-ml is highly desirable.