Data Scientist, Retail Strategy

1 day ago

St Louis, United States ZipRecruiter Full time

Job DescriptionJob Description

At Build-A-Bear we believe in the power of imagination, creativity, and memorable experiences. Our mission is to add a little more heart to life by creating unique experiences, lasting connections, and beloved memories that create lasting emotional connections. We are a multi-channel, site-based experience and entertainment company, with diversified categories and consumer segments. We are seeking a highly experienced and strategic Data Scientist, Retail Strategy to join our dynamic team.

As a Data Scientist specializing in Retail, you will be a key player in shaping data-driven strategies that fuel the success of our business. Leveraging your expertise in advanced analytics, machine learning, and statistical modeling, you will uncover actionable insights that drive key business decisions across the organization.

Responsibilities:

  • Research and development:
    • Conduct cutting edge research in machine learning and artificial intelligence
    • Develop novel algorithms and models to solve complex problems
    • Publish research findings in top-tier conferences and journals
  • Model Development:
    • Design, implement, and experiment with new machine learning models.
    • Test and validate models using rigorous scientific methods
    • Optimize models for performance, accuracy and scalability
  • Data Analysis and Experimentation:
    • Analyze large data sets to extract meaningful patterns and insights
    • Conduct experiments to evaluate the effectiveness of different approaches
    • Develop benchmarks and evaluation metrics for model performance
  • Knowledge Transfer and Cross-functional collaboration with teams including data engineers, software developers and domain experts.
    • Create clear and compelling visualizations that communicate insights to stakeholders at all levels
    • Translate research findings into practical applications and solutions
    • Mentor junior researchers and interns

Required Qualifications:

  • 5 years' experience Data Science or Machine Learning
  • Experience in a Retail environment
  • Statistics and Machine learning expert
  • Skilled with Python and Data Analysis
  • High level business acumen
  • Understanding of retail operations and strategy

Preferred Qualifications:

  • PHD or master's degree in computer science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
  • Strong publication record in top-tier AI/ML conferences and journals.
  • Experience in conducting independent research and leading research projects.
  • Background in applying AI/ML techniques to real-world problems in various domains (e.g., healthcare, finance, robotics).

Behavioral Traits for Success:

  • Drive, determination, and self-discipline
  • Technical problem-solving and ingenuity while working within approved organizational systems and technology
  • Strong commitment to tasks being completed correctly and on time
  • Understanding and collaborative
  • Thrives in a structured environment
  • Comfortable making decisions in area of expertise
  • Communication style is factual and sincere
  • Willingness to follow established policies, processes, and procedures
  • Comfortable working at a fast-paced environment

Working Environment:

  • Typical office environment with climate control and sufficient lighting, ergonomic desk/chairs
  • Hybrid work schedule
  • Corporate Office located St. Louis, MO
  • Occasional Domestic Travel
  • Able to lift >25 pounds

Your Performance Will Be Measured On:

Your performance will be measured by your ability to achieve annual department objectives and corporate goals which include but are not limited to the following.

  • Decision-making, judgment, and execution
  • Able to communicate complex issues in a clear and concise manner
  • Quality presentations that are engaging and offer actionable insight
  • Deadlines, Accuracy, and Quality
  • Creation of practical applications/solutions based on data
  • Stakeholder Feedback

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