Position: Data Scientist
Company: Mobalytics
Category: Data Science
Experience required: 3y
English level: upper

**Your Technical Skill Set**

Research expertise in a specific domain
We are looking for depth here. Show us you have spent infinite hours breaking apart something 90% of people likely think is irrelevant.
A PhD is a good start, but spending years working on data for a specific company is just as good.

Expertise in video games
Not necessarily in terms of skill proficiency since we have plenty of people with that expertise, but you need to game seriously and understand the vernacular.

Self sufficient with analysis tools
You need to be able use your toolset to interact and work closely with engineers in a development environment
Our current team works with: Python, Spark, Kafka, Airflow
Additional experience with SQL, NOSQL databases as well as Anaconda libraries is a plus

Here are some things that are a huge plus:
Golang experience
Aerospike experience
Deploying models in Google Cloud

Expertise with machine learning
Deep understanding of linear models, decision trees, and neural networks with the goal of finding new ways to apply that knowledge to generate meaningful insights.
Bonus points if this has been the day to day of your thesis for the last few years.

**What we are expecting from you**

Deliver data science and data engineering projects from idea to completion
Develop the idea, create a plan, layout the methodology, collaborate with other departments, execute the plan, and provide recommendations based on results.
Here are some examples:
Process games played on a specific patch and identify and bucketise patch related information for different purposes
Enhance our GPI(Gamer Performance Index) algorithm
Create recommendations based on players historical data
Thoroughly document data science processes and code, and develop strategies to ensure they are safe, secure, and reproducible
Visualize data to communicate results and findings to the product teams.
Help us to optimize legacy code and refactor existing data processing pipelines.