Data Scientist

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Organisation
Sage
Locations

London, United Kingdom

Application Deadline

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!

Experience has taught us that when our customers thrive, we thrive. As a team, we always start with what customers need. Through the good… and more challenging times. Innovating at pace so customers can manage their finances, operations and people. Every one of us shapes our culture at Sage - doing what’s right and succeeding together, united by our commitment to each other. We encourage each other to grow in our roles, in our careers and as individuals.

Follow us on our social media sites below to join in conversations about career tips, open positions and company news! #lifeatsage #sagecareers. If you would like support with your application (or require any adjustments) please contact us at careers@sage.com 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.

Job Description

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 Responsibilities

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 organisationBe the subject matter expert demonstrating mastery of the delivery and use of Data Engineering techniques and Science and its supporting technologiesEmpower internal stakeholders using the art of the possible and to gain new meaning from dataYou may be a fit for this role if:

You’re very comfortable with investigating open-ended problems and coming up with concrete approaches to solve them.You know when to use machine learning and when not to!You’re a deeply curious person.You often think about applications of machine learning outside of your work life.Preferred:

PhD in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative fields.Publications in top conferences.Experience writing complex SQL queries.You have deep experience with these things: logistic regression, gradient descent, regularization, cross-validation, overfitting, bias, variance, convex optimization, eigenvectors, relational databases, SQL, latency, computational complexity, sparse matrices.Technical / professional qualifications:
MS in Mathematics, Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative field.Real world application development experienceExperience in IT roles across development and architecture with demonstrable experience and knowledge across all IT domainsHands-on experience with technology that underpin Big Data and Data Integration including Hadoop, Spark, Scikit-Learn or other ML library, Statistics, CI/CD, coupled to standards including OData, Map/Reduce, Scala/R and/or Python
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