The Data Science team is key to Slice’s ongoing success. The data we have and the models we build are foundational to our platform and quite literally drive our business. Our work improves the customer experience, grows our market share and drives business outcomes. This is an opportunity to play an active part in delivering our data vision and determining how we get there. All that to say: Data Science work here at Slice is far from theoretical.
This ROLE develops and executes Data Science projects across the company. This means hands-on work at all points in the data lifecycle, including data wrangling and mining; feature engineering; model-building and testing; and implementation and communication. Your work will ensure that all our decisions are data-driven - this means that you will have a direct impact on the customer experience by influencing critical decisions on resource deployment and customer engagement. Our immediate applications include marketing analytics, fraud prevention, and risk/value modeling. (Our future applications are boundless!)
YOU are passionate about data. You enjoy being able to combine your analytical, technical and business skills in one role. You love solving problems and predicting behaviours. You are a collaborator and a communicator and are energized by working with multidisciplinary teams. You are a hands-on learner and are excited by the thought of moving past theory and examples to real-time data science work.
It would be awesome if you bring:
- An effective communication style with an ability to translate “the complex” to “the simple”. You are adept at data visualizations and have experience working with real-time data.
- 4+ years of practical experience using data, models, and common sense to solve tough problems in a collaborative environment. Experience deploying models is a plus!
- Working experience with Python, including Pandas, NumPy, scikit-learn, NLTK, and Keras/TensorFlow.
- An understanding of statistical and predictive modeling concepts, machine learning algorithms, clustering and classification techniques.
- Some exposure to one or more sub-fields of data science, especially GIS/spatial analysis; graph theory/network analysis; or natural language processing (NLP).
- Working experience with non-Excel BI tools such as Tableau, Looker, Superset, PowerBI, etc.
- You are comfortable building datasets using traditional relational databases and you’re familiar with alternative databases (noSQL, graph databases) or big data platforms such as Apache Spark.
- University degree in engineering, applied statistics, data mining, machine learning, mathematics or a related quantitative discipline.