Data Scientist

Full time

Employment Information

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Who we are

Imagine working in a place where continuous improvement and innovation is celebrated and rewarded; where fast-paced, high-impact teams come together to positively drive results for one of the largest & most iconic brands in the world.

As the only rapidly growing retailer, you may know us as your friendly neighborhood store. You probably know our familiar name, have seen our pervasive logo, and have tried our highly sought-after products, such as Slurpee® and Big Bite®. “Brain Freeze” is a 7-Eleven registered trademark for our 53-year old Slurpee® and with over 77,000 stores globally (more than any other retailer or food service provider), we sell over 14 million a month.

But there’s a lot more to our story and much more left to be written. We are transforming our business, ensuring we are customer obsessed and digitally enabled to seamlessly link our brick and mortar stores with digital products and services.

At 7-Eleven the entrepreneurial spirit is in our DNA and has been ever since our inception 90+ years ago. It’s what drove us to invent the convenience industry in 1927 by envisioning how a simple ice dock could provide household staples such as milk and eggs to better serve the needs of our customers.

Today we are redefining convenience and the customer experience in big ways…we are fundamentally changing our culture and we want talented, innovative, customer obsessed, and entrepreneurial people like you to come make history with us.

How we lead

At 7-Eleven we are guided by our Leadership Principles.

  • Be Customer Obsessed
  • Be Courageous with Your Point of View
  • Challenge the Status Quo
  • Act Like an Entrepreneur
  • Have an “It Can Be Done” Attitude
  • Do the Right Thing
  • Be Accountable

Each principle has a defined set of behaviors which help guide the 7-Eleven team to Serve Customers and Support Stores.

About This Opportunity

Responsibilities We are seeking a talented Data Scientist to join our team. As a Data Scientist, you will play a crucial role in analyzing complex datasets, extracting valuable insights, and contributing to data-driven decision-making processes. The ideal candidate should possess a strong background in statistics, machine learning, and data analysis, coupled with excellent programming skills.


  • Data Analysis and Interpretation:
    • Analyze large and complex datasets to identify trends, patterns, and insights.
    • Interpret and communicate findings to both technical and non-technical stakeholders.
  • Machine Learning Model Development:
    • Develop and implement machine learning models for predictive and prescriptive analytics.
    • Evaluate and fine-tune models to ensure optimal performance.
  • Data Cleaning and Preprocessing:
    • Clean and preprocess raw data to prepare it for analysis and modeling.
    • Address missing or inconsistent data to maintain data quality.
  • Statistical Analysis:
    • Conduct statistical analysis to validate hypotheses and draw meaningful conclusions.
    • Collaborate with cross-functional teams to design and execute experiments.
  • Feature Engineering:
    • Identify relevant features and variables for model development.
    • Optimize feature selection to enhance model accuracy.
  • Data Visualization:
    • Create visually appealing and insightful data visualizations to facilitate understanding.
    • Use tools like PowerBI, matplotlib, or others to communicate complex data stories.
  • Collaboration and Communication:
    • Work closely with other teams to understand business requirements and objectives.
    • Effectively communicate technical concepts to non-technical stakeholders.
  • Continuous Learning:
    • Stay updated on the latest advancements in data science and machine learning.
    • Apply new techniques and technologies to improve existing models and processes.


  • Programming languages: Python, R etc.
  • Data manipulation libraries: For example Pandas
  • Data visualization tools and libraries: PowerBI, matplotlib, seborn etc.
  • Machine learning libraries: Scikit-learn, Tensorflow etc.
  • Databases: SQL Server, BigQuery etc.
  • Statistics and Machine Learning concepts: Supervised and Unsupervised learning, Reinforcement Learning, Design of experiments etc.

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