Data Scientist to Production.AI

Company Description

Are you a Data Scientist who wants to develop and improve AI methods as well as explore how machine learning can be applied to support decision making on a global scale in the H&M Group?

We are on an exciting journey to meet and exceed our customers' expectations today, tomorrow and in the future. Rapid technological development and new customer behaviours are transforming the fashion retail industry. To cater the individual needs and desires of our millions of customers, Business Tech delivers technological solutions for the entire value chain for all our brands.

There are endless opportunities to grow, regardless of whether you are interested in developing functional depth or business acumen. Just let your skills, ambition and imagination drive you.

Job Description

Do what you are most passionate about and drive change together with us! The job of a Data Scientist within the H&M Group comes with a lot of responsibility – and lots of fun. In your role you will work in collaboration with our business and engineering people both within your team and supporting teams, on creating value from data. Often the work requires solving complex problems by turning vast amounts of data into business insights through advanced analytics, modeling and machine learning.

The team you will be a part of is Production.AI. An enabling team, delivering value to our users within Production Organization, using our current and historical data to provide capabilities and features for quicker decision making, optimization of flows & efficient negotiations with our suppliers.

You will be working in a cross-functional agile and innovative team with possibilities to explore, test, fail and learn together with other data scientists, data analysts, engineers and business stakeholders, to build the AI ecosystem within H&M.

Production.AI provides solution to different tools, applications and systems within H&M group, leveraging historic data across Production organization to help in price negotiation, supplier recommendation, image-based product search, carton fill rate, to name a few use cases. Starting in 2019 with the first use case called Comparable Product Engine, providing the best match, given the data available, that matches historically with what the user is currently negotiating prices on.

Furthermore, you will:

• Work in collaboration with business stakeholders, analysts and engineers, both within your team and supporting other teams, on creating value from data.
• Be involved in all stages, from data exploration and building proof of concept to operationalization and handover to other teams.
• Proactively fetch information from various sources, create data sets and do analysis to better understand current business performance and processes.
• Come up with suggestions on how business processes can be improved with the help of data modeling and what methods can be tested and applied.
• Design, build, test and iterate proof of concepts, with clear focus on business value and with the desired end product in mind.
• Take part in researching and developing state-of-the-art methods which can make a big impact on the business.
• Develop understanding of business obstacles, create solutions based on advanced analytics and draw implications for model development
• Design and build explorative, predictive- or prescriptive models, utilizing optimization, simulation and machine learning techniques

Qualifications

We believe that you are communicative, strategic and have a determination to never give up. You are a team player, self-sufficient enough to take initiative and have a positive attitude. Apart from that we see that you:

• Have a strong foundation in analytics, mathematical modeling, computer science and mathematics - coupled with an interest in business. You have the ability to understand business pain points and suggest how machine-learning empowered analytical tools can help to solve them.
• Have at least 3 - 5 years working experience in data science where you acquired broad knowledge of different data science techniques and you have working knowledge of Python.
• Our journey will lead to something new and exciting - we will test, fail and learn. Therefore, we believe you are flexible problem-solver and able to work in an environment that is not yet formalized.
• Strong abilities in all or several parts of the advanced analytics development process: from exploring the potential of a raw data set, to building models, to scaling and stabilizing these models etc.
• Experience with common data science toolkits, such as Python, Pandas, Spark, R, NumPy, Pytorch, TensorFlow, Scikit-Learn etc.
• Experiences in BI Analytics Tools, e.g. PowerBI / Tableau
• Proficiency in using query languages such as SQL, Hive, Pig

In addition, we appreciate if you have:

• Experience in operationalizing machine learning models
• Familiarity with best practice software development techniques
• Familiar with agile development methodologies
• Able to handle detailed analysis, work independently and in a structured way

Additional Information

At H&M we have a strong, value-driven culture characterized by believing in people, teamwork and an entrepreneurial spirit. Here you can be yourself and have room to grow. You’ll be part of an important project at H&M with significant opportunities to influence and make a difference. With the right mindset and ambition, you’ll find plenty of room to grow. We welcome your application regardless of gender, age, gender expressions, sexual orientation, disabilities, religious beliefs, origin or background.

Do you think we are a match? We hope so!

This is a fulltime position with placement in Stockholm. Please apply with CV and Cover Letter (in English) as soon as possible.

We review applications on an ongoing basis. Due to GDPR, we don’t accept applications via email. And we love code! If you have contributed to Github project(s), also send those to us together with the CV. We are more than happy to take a look!

For questions about the position, please contact Talent Acquisition Sourcer [email protected].