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Buxton

Predictive Data Analyst (Contract Position - 12 months)

Sorry, this job was removed at 02:24 p.m. (EST) on Monday, Jun 01, 2026
Remote
Hiring Remotely in US
45-60 Hourly
Remote
Hiring Remotely in US
45-60 Hourly

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Audiense is a next-generation analytics-to-action platform that helps organizations deeply understand and strategically activate their consumers. We’ve brought together the strengths of three category-leading brands — Buxton, Elevar, and Audiense — into one integrated, insight-driven solution: 


  • Buxton by Audiense – 30+ years of predictive modeling and location intelligence expertise. 
  • Elevar by Audiense – Industry-leading eCommerce tagging, event tracking, and revenue attribution tools. 
  • Audiense – Award-winning audience segmentation and social consumer intelligence for global brands. 


Together, we help brands across industries, from retail to CPG to media, make faster, smarter, and more confident decisions that fuel real-world growth. 


This position primarily supports the Buxton by Audiense line of business. 


We are seeking a highly analytical and self-directed professional to support advanced analytics initiatives across client engagements. This role is ideal for someone who thrives in fast-paced environments, can ramp up quickly with minimal oversight, and is comfortable owning analytical work from problem definition through delivery. 


The ideal candidate brings strong experience in quantitative analysis, statistical modeling, and Python-based analytics workflows. This individual will work cross-functionally to uncover insights, develop analytical solutions, and communicate findings directly to internal stakeholders and clients. 


This role is best suited for someone who enjoys solving complex business problems, working independently, and translating data into strategic recommendations. 


What You’ll Do 

• Lead end-to-end analytical projects, from exploratory analysis and methodology selection through insight development and final delivery 

• Develop quantitative models and advanced analytical solutions to support client decision-making across marketing, real estate, and consumer strategy initiatives 

• Analyze large and complex datasets to identify trends, behavioral patterns, and actionable business insights 

• Design and execute ad hoc analyses using Python, SQL, and statistical methodologies 

• Translate analytical findings into clear business recommendations for both technical and non-technical audiences 

• Work directly with internal stakeholders and clients to scope analytical questions, prioritize opportunities, and deliver strategic insights 

 Operate independently across multiple projects in a fast-paced environment with minimal oversight 

• Develop analytical outputs, visualizations, and reporting solutions to support business decision-making 

• Collaborate cross-functionally to identify opportunities for deeper analysis and expanded client value 


What You Bring 

 Bachelor’s degree in Data Science, Statistics, Economics, Mathematics, Computer Science, Business Analytics, or a related quantitative field required; advanced degree preferred 

• Strong experience in quantitative analytics, statistical modeling, and data interpretation 

• Advanced proficiency in Python for data analysis, modeling, and automation 

• Experience working independently with large and complex datasets across multiple data sources 

• Ability to structure ambiguous business problems into analytical frameworks and actionable recommendations 

• Strong analytical thinking and problem-solving skills with a high level of intellectual curiosity 

• Excellent written and verbal communication skills 

• Ability to quickly ramp into new business contexts and contribute with minimal onboarding 

• Comfortable operating in fast-moving environments with evolving priorities 

• Experience in client-facing, consulting, or cross-functional environments a plus 

 Proficiency with SQL and data querying concepts preferred 

• Experience with predictive modeling, statistical analysis, or advanced analytics techniques preferred 

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