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Data Scientist (Columbus)
Data Scientist (Columbus)Insight Global • Columbus, Ohio Metropolitan Area, United States
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Data Scientist (Columbus)

Data Scientist (Columbus)

Insight Global • Columbus, Ohio Metropolitan Area, United States
[job_card.variable_days_ago]
[job_preview.job_type]
  • [job_card.full_time]
[job_card.job_description]

Required Skills & Experience

  • Proficiency in SAS , SQL , and at least one programming language such as Python or R
  • Strong foundation in statistical modeling , data mining , and machine learning techniques
  • Demonstrated ability to communicate complex analytical insights clearly to both technical and non-technical stakeholders
  • Proven experience in cross-functional collaboration within product, engineering, and business teams

Nice to Have

  • Masters degree in Data Science, Statistics, Computer Science, or a related field
  • Familiarity with cloud-based analytics platforms (AWS, Azure, GCP)
  • Experience with big data technologies (Hadoop, Spark) and data visualization tools (Tableau, Power BI)

    • Job Description

    A client of Insight Global is seeking multiple Data Scientists to join a high-impact analytics team. This role is ideal for professionals passionate about leveraging data to drive strategic decisions and innovation.

    Key Responsibilities

  • Apply advanced analytics and machine learning techniques to extract actionable insights from large, complex datasets
  • Design and execute large-scale experiments and build predictive models to solve critical business challenges
  • Conduct research on cutting-edge AI / ML methodologies , including deep learning and natural language processing
  • Define and implement requirements for training and optimizing deep learning models
  • Develop and articulate a data strategy roadmap , positioning data as a core corporate asset
  • Influence product development through data-driven recommendations and storytelling
  • Champion best practices in analytics , fostering a culture of data literacy across teams
  • Own the end-to-end model lifecycle : from business requirement gathering, data sourcing, model development, validation, and deployment into production environments
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