logo

Senior Game Data Scientist

  • Anywhere

Company :
Zynga

Location :
Austin, Texas

Expiry Date :
Sun, 06 Dec 2020 23:59:59 GMT

Apply Job :
Open Link

Description :
Position at Zynga Zynga is a leading developer of the world’s most popular social games that are played by millions of people around the world each day. To-date, more than 1 billion people have played our games across Web and mobile, including FarmVille, Zynga Poker, Words With Friends, Harry Potter: Puzzles & Spells, Merge Dragons!, Empires & Puzzles, Hit it Rich! Slots, Toy and Toon Blast, and CSR Racing. Zynga’s data science team uses our unique and expansive data to model and predict user behavior, making our games more personalized and more fun to play! We continually strive to better understand our players, and provide them with experiences that surprise and delight. Here’s where you would come in: as a member of Zynga Poker, you will join a Forever Franchise, a Zynga designation for games that bring in at least $100 million in revenue per year. We want you to identify and formalize problems predicting user behavior, then use our modern and ever-evolving tech stack to build and implement your models to find solutions. Be innovative, be creative, use every bit of our key commodity – data. Millions of people play Zynga games every day, so our data is tremendously rich, and we have a lot of it! We will rely on you to communicate your findings to your peers – both technical and non-technical. Your solutions will need to be demonstrably impactful and visual. You will work with our game teams to put your models into production. You will collaborate with Product Managers, Game Designers, and Engineers to deliver business impact. And, change current practices in line with new findings and insights. Responsibilities: Leverage our modern tech stack, AWS (Redshift & Kinesis), DataBricks and PySpark, Airflow, and Tableau to identify opportunities to improve the experience that Zynga provides to its players predictive modeling and Data Mining techniques for a variety of user modeling tasks within Zynga’s Game Network Work closely with game teams to design, test, verify and implement machine learning models with Zynga’s games that impact the daily life of millions of users Design and evaluate novel scalable approaches to experiments for gameplay, using our in-house experimentation platform Specifically, you may encounter projects focused on: modeling the Poker economy through simulation, personalizing our in-game store using recommendation algorithms, or combating fraud and abusive behavior through near-real time interventions. Required Skills and Experience: BS in Computer Science, Math, Statistics, Economics, or other quantitative field; Masters or PhD strongly preferred 3+ years of work experience in data science, machine learning or analytics roles Demonstrated experience with some or all of the following: machine learning, data mining, predictive modeling, statistics, experimental design, computational analytics, econometric modeling, data visualization Fluent in SQL, Python, and other programming languages; Experience in applying machine learning on large datasets, preferably using Spark on Databricks Strong written and oral communication skills What we offer you: Zynga Stock RSUs and Bonus Plan Full medical, dental, vision benefits as well as life insurance Generous Paid Maternity/Paternity leave Open vacation policy for all full time employees Flexible working hours on many teams Work alongside driven individuals towards a common goal Zynga is an equal opportunity employer. We are proud of our diverse community; we do not discriminate on the basis of race, sex, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, medical condition, disability, or any other class or characteristic protected by applicable law. We welcome candidates, players, employees, and partners from all backgrounds. Join us! Zynga will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law.