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Data Scientist

Job in Nashville, Davidson County, Tennessee, 37247, USA
Listing for: Staffing Agency Recruitment
Full Time position
Listed on 2026-06-14
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist, Data Engineering, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below

Location: Remote (Nashville, TN strongly preferred)

Job-Type: Full-Time

Role Overview:

We are looking for a full-time Data Scientist to join the Data Analytics team. Leveraging advanced analytical techniques, statistical modeling, and/or machine learning, you will partner with the business to uncover opportunities, optimize performance, and drive data-informed outcomes.

This role will partner closely with marketing, sales, operations, supply chain, corporate, and finance teams to identify opportunities, develop predictive and prescriptive models, and deliver actionable insights that improve revenue growth, operational efficiency, and margin performance. The ideal candidate combines strong technical expertise in statistical modeling and advanced analytics with the ability to translate complex data into clear, business-relevant insights for marketing and sales teams.

This individual will work with large, complex datasets spanning manufacturing, distribution, pricing, and customer behavior. While this role does not require hands-on data engineering responsibilities, it demands close collaboration with the Data Engineering team. Candidates should have a solid understanding of core data engineering concepts to effectively partner, translate business needs, and ensure alignment across data workflows and infrastructure.

Requirements

Requirements:

  • 5+ years of progressive experience in data science, advanced analytics, or a closely related field, with a strong preference supporting marketing or commercial teams
  • Experience in the development of machine learning models and AI frameworks
  • Experience working with data visualization and business intelligence tools (e.g., Tableau, Power BI, or similar tools)
  • Experience working with enterprise data platforms (e.g., Snowflake, Databricks, Cloudera, Big Query)
  • Experience working with data from large enterprise applications (e.g., ERP, CRM or operational systems)
  • Fluency in Python and SQL (required); R (optional)
  • Demonstrated experience developing and validating statistical models, machine learning algorithms, and advanced analytical solutions using large, complex datasets
  • Strong competency in statistical & quantitative methods (e.g., hypothesis testing, regression, probability theory, experimental design, etc.)
  • Demonstrated experience with experimentation and causal analysis
  • Demonstrated experience with experimental design, model evaluation, and performance measurement
  • Strong understanding of data pipelines, ETL processes, and data architecture
  • Proven success in supervised and unsupervised learning (e.g., regression, classification, clustering)
  • Strong understanding of AI, its potential roles in solving business problems, and the future trajectory of generative AI models
  • Excellent presentation, communication, and stakeholder management skills, with the ability to explain technical concepts in business terms to a diverse audience
  • Highly self-motivated with proven ability to operate autonomously, managing multiple priorities in a fast-paced environment
  • Willingness and ability to learn new technologies on the job with a continuous learning and innovation mindset
  • Bachelor’s degree in computer science, mathematics, data science, statistics, or a related quantitative field (Master’s degree preferred)
  • Occasional travel up to 15% of time
  • Occasional exposure to a plant environment

Preferred Qualifications:

  • Experience working with SAP (S/4 HANA, ECC, BTP, etc.)
  • Experience working with cloud platforms (AWS, Azure, or GCP)
  • Experience in text analytics, image recognition, graph analysis, or other specialized ML techniques (e.g., deep learning)
  • Experience in manufacturing, building products, or construction-related industries
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