Mid to Senior Machine Learning Engineers
The success of Argos’s technology platform is getting bigger and better every year. After last year’s very impressive data almost hitting 1B visits to our digital channels and currently getting over 20M unique visitors every week. We are on the mission to build out our Machine Learning Function to take us to the next level!
We are building are own Data Science and Machine Learning capability, aiming to automate and improve the millions of decisions that we need to make daily to provide great availability of products to our customers across multiple fulfilment propositions and at a competitive price.
Argos Technology supports the complex operation of over 50,000 products being sold on a regular basis on multiple sales channels (Website, Mobile Apps, Instore Digital Tablets and General Retail) over 1000 suppliers, 900 stores, 10+ distribution centres and global sourcing. For us, Machine learning is not buzz words, with board level commitment the business is giving this real focus as the key to fixing problems within our supply chain.
As a Machine Learning Engineer in the Commercial Supply function, you will join a team building predictive and prescriptive models to optimise commercial supply trade-offs, specifically but not limited to demand forecast, store assortment, inventory, dynamic pricing and customer service. So, if you can source, extract and prepare data, test algorithms and be passionate in your search for data solutions. This could be the perfect role for you!
Ideal candidates for this role will have prior industry experience applying ML on a wide range of optimisation problems (supply chain/transportation optimisation, demand forecasting, collaborative filter/recommendations, NLP fundamentals and payment fraud).
The Full Hadoop product suite including but not limited to Spark and Hive, deploying all the way to production on our Hadoop cluster.
Required programming languages: R, Python
Required: Solid understanding of Machine Learning theory and practice.
Desirable: Experience with Deep Learning libraries such as Tensorflow and Keras.