Agile Resources, Inc.

Data Scientist

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist in Denver, CO, on a 9-month contract at $50-$70/hr W2. Requires 3-5 years of experience in data science, strong Python and SQL skills, and experience with machine learning models.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
560
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πŸ—“οΈ - Date
March 11, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
Hybrid
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πŸ“„ - Contract
W2 Contractor
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
Greenwood Village, CO
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🧠 - Skills detailed
#Clustering #Leadership #Libraries #Datasets #Regression #Cloud #AWS (Amazon Web Services) #Mathematics #AI (Artificial Intelligence) #Documentation #Data Exploration #Neural Networks #SQL (Structured Query Language) #Scala #Data Manipulation #Python #Data Mart #Programming #"ETL (Extract #Transform #Load)" #Statistics #Tableau #Data Science #Data Warehouse #NLP (Natural Language Processing) #Data Architecture #ML (Machine Learning) #Visualization #Computer Science
Role description
Hybrid in Denver, CO: 4 days in office with 1 day remote 9 month contract Compensation: $50-$70/hr W2 No C2C or sponsorship will be offered for this role now or in the future. We are seeking a Data Scientist to help improve and enhance the customer experience through data-driven insights and machine learning solutions for our client. In this role, you will partner with product, operations, and leadership teams to analyze complex datasets, develop predictive models, and translate analytical findings into actionable business recommendations. This position is ideal for someone who enjoys solving real-world business problems, working with large datasets, and communicating insights to both technical and non-technical audiences. What You’ll Do β€’ Support initiatives focused on improving and simplifying the customer experience through advanced analytics. β€’ Gather requirements and translate business problems into analytical approaches. β€’ Perform data exploration, cleansing, and feature engineering across structured and unstructured datasets. β€’ Develop, prototype, validate, and deploy machine learning and statistical models. β€’ Conduct analyses ranging from exploratory analytics to advanced modeling techniques. β€’ Communicate insights and recommendations clearly through presentations, visualizations, and written documentation. β€’ Partner with product owners, stakeholders, and leadership to guide data-informed decision making. β€’ Troubleshoot and optimize models and analytical approaches to improve performance and scalability. β€’ Collaborate with cross-functional teams to productionize analytics solutions. Required Qualifications Skills & Experience β€’ 3–5 years of experience in data science, advanced analytics, or statistical modeling. β€’ Strong programming experience in Python (including common data science libraries). β€’ Experience building and applying machine learning models such as: β€’ Regression models β€’ Decision trees and ensemble methods β€’ Clustering techniques β€’ Support Vector Machines β€’ Neural networks β€’ Strong SQL and data manipulation skills. β€’ Experience working with large datasets and data transformation pipelines. β€’ Understanding of data architecture concepts, including data warehouses and data marts. β€’ Ability to communicate analytical findings to both technical and business audiences. β€’ Strong problem-solving and analytical thinking skills. Education β€’ Bachelor’s degree in Computer Science, Statistics, Operations Research, Mathematics, or a related quantitative field (or equivalent experience). Preferred Qualifications β€’ Cloud platform experience (AWS preferred). β€’ Experience with data visualization tools such as Tableau. β€’ Exposure to text analytics or natural language processing techniques. β€’ Experience in telecommunications or customer-focused digital products. β€’ Master’s degree in Data Science, Machine Learning, AI, or related field. Working Environment β€’ Highly collaborative and innovative team environment. β€’ Opportunity to work with modern data tools and large-scale datasets. β€’ Cross-functional exposure across product, engineering, and business teams. What Success Looks Like β€’ Deliver actionable insights that improve customer experience and business outcomes. β€’ Build reliable models that move from experimentation into production. β€’ Communicate complex findings in a clear, business-friendly way. β€’ Contribute to a data-driven culture through collaboration and innovation.