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Electronic Arts Off Campus Hiring - Data Science Internship
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Electronic Arts Off Campus Hiring – Data Science Internship

Electronic Arts Off Campus Hiring: Electronic Arts Inc. (EA), the world’s leader in interactive entertainment, is seeking a highly skilled Data Science Intern to join its Hyderabad team for a stimulating three-month program. This position offers a thrilling possibility to participate in the most cutting-edge data science research projects and contribute to innovative solutions that improve the gaming experience for millions of players around the world. The ideal candidates are enthusiastic about data, knowledgeable in Python and keen to make use of machine learning as well as analytics within a dynamic and creative setting.

Electronic Arts Off Campus Hiring
Electronic Arts Off Campus Hiring

Overview

  • Job Title: Data Science Intern
  • Company: Electronic Arts Inc. (EA)
  • Location: Hyderabad, Telangana, India
  • Duration: 3 Months
  • Work Arrangement: In-Office, Full-Time
  • Working Days: 5 Days a Week

Responsibilities

As a Data Science Intern at EA, the role requires direct involvement in projects that are driven by data that have real-world consequences. The primary responsibilities are:

  • Develop robust Python Modules: Develop and maintain Python-based modules and notebooks that handle data ingestion and feature engineering, and model-training using libraries such as Pandas, NumPy, and scikit-learn.
  • Write high-quality code: Write clean, reliable code by leveraging the power of object-oriented programming (OOP) types, type hints or docstrings, tests for integration and unit testing. Take part in reviews of code and follow Git-based workflows that allow for the control of your version.
  • Exploratory Data Analysis (EDA): Investigate data to identify the problem, develop hypotheses and carry out EDA using innovative visualisations (using Matplotlib and plotly) and summarised statistics.
  • Machine Learning Application: Create, assess and improve the baseline and advanced machine learning models. Choose appropriate metrics, create experiments, and use cross-validation methods to ensure reliable performance.
  • SQL as well as Data Pipelines: Make use of SQL to extract data efficiently, transform, and aggregate. Work with other teams to build solid data pipelines that can aid in reporting and analytics scenarios.
  • Connect Insights: Create captivating narratives as dashboards, reports, and reproducible notebooks that present the results. Convert complex results into practical business and product suggestions for non-technical and technical users.
  • Help to improve Best Practices: Enhance the lifecycle of development for your team through automation, continuous integration (CI) and thorough documentation. Make suggestions for improvements to workflows and procedures.

Requirements

The ideal candidate has an excellent foundation on data science, programming and analytical abilities. The requirements for this include:

  • Python Experience: proficiency in Python for data science-related tasks, with hands-on experience using NumPy, pandas and scikit-learn. Ability to write reused classes and functions, troubleshoot efficiently, and comprehend the fundamentals of packaging.
  • Coding Basics: A solid understanding of data structures, algorithmic, OOP, and modular design, along with unit tests (e.g. Python, the pytest program). Experience using Git workflows (GitHub/GitLab), which includes branching, pushing, and merging.
  • Machine Learning Knowledge: A solid understanding of techniques for supervised learning (e.g. logistic or linear regressions, decision trees and ensembles), regularisation, bias/variance trade-offs, Cross-validation and feature scale/encoding and metrics for evaluating models (e.g. AUC/ROC, F1, MAE/RMSE).
  • Statistics Proficiency: Skills in statistical techniques, such as the testing of hypotheses, sampling confidence intervals, and distributions. Ability to choose and interpret the appropriate statistical tests.
  • SQL Skill: Strong SQL understanding of data extraction, Joins, aggregations, and the basics of query optimisation.
  • Data Wrangling and Visualisation: Expertise in handling missing data, such as outliers and joins, pivots and time-series/tabular transforms. Experience in creating clear visuals with Plotly or matplotlib, accompanied by a concise narrative summary.
  • Mindset of Problem-Solving: Ability to identify issues, create experiments, offer incremental value and clearly record your decisions.
  • Communication Skills: Capability to write concise documents and notebooks along with clear explanations in a way designed for technical as well as non-technical users.

How to Apply

The candidates who are interested should submit their resume with a list of the relevant skills and a Portfolio (e.g., GitHub repositories or projects in data science). Applications from early on are encouraged since positions are not guaranteed.

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