Position: Data Scientist To Product Team
Company: 2K-Group
Category: Data Science
Experience required: 5y
English level: upper

Project: SaaS-based, data-driven eCommerce platform on AI-powered and fully cloud-native technology. North America market.

# What you’ll be doing

- *Understanding the problem*: what is the current state-of-the-art, and what’s wrong with it? Review the literature, look at the competition, talk to stakeholders - understand the problem.
- *Getting the data*: do we have the data? If not, how do we collect it? How/where should we store it? You’ll work with our data engineers to answer these questions and get the data you need.
- *Explore and Prepare*: despite our best efforts, our data is dirty just like everyone else’s. So yes, there will be some cleansing to do, but through exploration and over time you’ll develop an intuition for what the data can and can’t do, and the best practices for working with it.
- *Analysis and Modeling*: you’ll identify and select features, build and optimize classifiers using machine learning techniques. Expect to also develop custom data models and algorithms to apply to data sets.
- *Test and Deploying*: through testing you’ll make sure your models will work in production, and that the services you build will operate at scale.
- *Evaluating and Iterating*: the models work and they scale, but did they have the intended effect? Why or why not? How do we make it better?
- To do these things, you’ll use **Google Cloud Platform’**s managed data services (like Composer, Dataflow, Dataproc, BigQuery) and modern cloud technologies like Kubernetes to deploy the tools and services you need.

# ***Skills you already have***

- You’re comfortable with freedom and autonomy: you own your work and do what it takes to drive it forward. You can independently learn new technologies, prototype and propose software design and solutions.
- Proficiency in one or more of the following: Python, Go, Java, Scala, SQL, JavaScript.
- Deep understanding of machine learning techniques and algorithms: k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Deep understanding of Deep Learning technique (NLP, Computer Vision) and tools (Tensorflow, PyTorch)
- Familiar with data visualisation tools and libraries, such as D3, Matlibplot, Plotly, Tableau.
- Good applied statistical skills, such as distributions, statistical testing, regression, etc.
- Deep understanding supervised, and un-supervised learning and model explainability

Full Time Remote Position.