Data Analyst (visa- toby)

Jam City•Culver City, CA

14d

About The Position

The Data Analyst will apply industry knowledge to creatively analyze data and drive project and studio improvements using statistical methods. This role involves leading complex analytics projects, leveraging data warehousing, architecture, modeling, and ETL processes with tools like SparkSQL and Python to create scalable data views. The analyst will utilize advanced analytics techniques, including statistical simulations and machine learning algorithms (e.g., logistic regression, boosted trees, random forest), to generate intelligence, predict player gaming behavior, and evaluate feature performance. Key responsibilities include extracting and analyzing complex datasets to understand game metrics, inform hypotheses, and design analytical experiments. The position also requires performing ad hoc analysis to identify patterns in player behavior, measure feature performance, support user acquisition, and resolve data issues using R, Python, Excel, and SparkSQL. The analyst will identify and implement operational improvements in data tasks and processes, create and distribute data and insights through regular and ad-hoc reports using platforms like Tableau, Excel, and SQL, and collaborate with executives and managers to clarify objectives and solve problems. Additionally, the role involves formulating mathematical or simulation models to improve game performance and communicating complex statistical findings to various stakeholders for decision-making.

Requirements

Masterâ€™s degree, or its equivalent, in Business Statistics, Business Analytics, Data Science, Mathematics or related field
One (1) year of experience in business analytics
Experience in data analytics applications
Experience in statistical computing and data visualization
Experience in predictive modeling
Experience in statistical learning methods for business decision-making

Responsibilities

Apply industry knowledge to come up with creative approaches to analyzing data and making project and studio improvements based on statistical methods.
Lead complex analytics projects utilizing data warehousing, data architecture, fact and dimension tables, data modeling and ETL processes to extract and transform raw data through various standardization and cleansing techniques to develop structured and efficient data views in a scalable way using tools like SparkSQL, Python.
Using advanced analytics techniques like statistical simulations and complex machine learning algorithms (logistic regression, boosted trees, random forest etc.) to generate valuable intelligence, predict playerâ€™s gaming behavior and find impacting factors, writing scripts in R/Python to evaluate feature performance pre-release using probabilistic distributions and rule-based machine learning systems.
Extract and analyze complex data sets to understand game metrics in order to inform hypotheses, and to create analytical experiments with clear and measurable success goals.
Perform ad hoc analysis as required to identify complex patterns affecting player behavior, measure feature performance and product health, support user acquisition and resolve data issues using R, Python, Excel and SparkSQL.
Use data tools to identify and implement analytics and operational improvements in efficiency of repetitive data tasks, pipelines, processes both on project specific level and studio wide.
Create, manage, and distribute data and insights via regular and ad-hoc reporting utilizing reporting and analysis platforms such as Tableau, Excel, SQL.
Create reports summarizing business intelligence data for review by executives and managers; collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives.
Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters in order to improve game performance.
Collaborate cross-functionally with other teams to communicate complex statistical findings and insights in business context to different teams and stakeholders for decision making.