CBase Inc

Data Scientist @ St. Louis, MO - Permanent Role - Hybrid Role

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist in St. Louis, MO, offering a full-time, hybrid position. Requires a Master's Degree, 2+ years in predictive modeling, proficiency in R/Python, and experience with TensorFlow/PyTorch. Expected duration is over 6 months.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
Unknown
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πŸ—“οΈ - Date
June 18, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
Hybrid
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πŸ“„ - Contract
Unknown
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
St Louis, MO
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🧠 - Skills detailed
#Statistics #Data Science #Python #Unix #Data Analysis #Deep Learning #PyTorch #R #Automation #Libraries #SQL (Structured Query Language) #Linux #TensorFlow #Security #Documentation #"ETL (Extract #Transform #Load)"
Role description
Hybrid Role - Mostly Remote Virtual Interviews Position: Data Scientist Location: St. Louis, MO Duration: Full-time Job Description: About The Role β€’ The Data Scientist is a key driver of innovation, transforming data into actionable insights that improve business processes. β€’ In this role, candidates will develop cutting-edge analytical productsβ€”creating algorithms for automation, building predictive models, designing experiments, and applying causal inference techniques to observational data. β€’ You’ll also harness mathematical optimization to identify the most profitable business strategies. Success in this position requires strong collaboration with both technical and nontechnical teams to ensure the creation, delivery, and adoption of impactful analytical solutions.ons Responsibilities: β€’ As a Data Scientist focused on revenue management, you will design and deploy advanced deep learning models to forecast demand. β€’ These models will enable branch-level decision-making, helping maximize revenue by leveraging historical trends and predictive analytics. β€’ In this role, you will collaborate closely with cross-functional teams to develop and implement analytical solutions that drive measurable business impact. β€’ Collaborate with the team to design and deliver analytical solutions that drive business impact β€’ Extract, clean, and manipulate structured and unstructured data from multiple sources β€’ Perform exploratory data analysis to identify patterns, trends, and insights β€’ Develop predictive models to support data-driven decision-making β€’ Design and oversee experiments, ensuring accurate execution and interpretation of results β€’ Apply causal inference techniques using observational data to uncover relationships β€’ Prepare and deliver clear documentation of methodologies, findings, and recommendations β€’ Create and present insightful reports and presentations for technical and nontechnical audiences β€’ Partner with cross-functional teams to implement and operationalize analytical solutions Equal Opportunity Employer/Disability/Veterans: Required Qualifications: β€’ Must be presently authorized to work in the U.S. without a requirement for work authorization sponsorship by our company for this position now or in the future β€’ Must reside in St. Louis, Missouri, or immediate surrounding area β€’ Must have a Master’s Degree in a Statistical or Mathematical field (e.g., Engineering, Social Science, or Statistics) β€’ Must have two (2 plus) years of experience with predictive models, statistical inference, and deep learning β€’ Must have experience using libraries like TensorFlow or PyTorch β€’ Must have experience preparing and giving presentations to technical and nontechnical audiences β€’ Must have proficiency in R or Python β€’ Must be committed to incorporating security into all decisions and daily job responsibilities Preferred Qualifications: β€’ Doctorate Degree in a Statistical or Mathematical field (e.g., Engineering, Social Science, or Statistics) β€’ Experience designing experiments β€’ Experience exploring and visualizing data β€’ Experience using Linux/Unix β€’ Experience using SQL β€’ Experience working with data (merging, recording, etc.) from a variety of sources/formats β€’ Experience working with observational data to attempt causal inference (e.g., matching, weighting, etc.)