We're seeking an experienced Data Scientist to support applied analytics and ML initiatives across our established cloud data platform. As a senior individual contributor, you'll work hands-on across modeling, experimentation, and production deployment. You'll have a strong understanding of commercial mindsets and be comfortable taking models from idea through to production.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
We’re working with a digital client looking to bring on an experienced Data Scientist to support applied analytics and ML initiatives across an established cloud data platform.
You’ll join an existing delivery team as a senior individual contributor, working hands on across modeling, experimentation, and production deployment, with visibility across multiple business domains.
This suits someone practical and commercially minded, comfortable taking models from idea through to production.
Six month initial contract with strong likelihood of rolling extensions.
Competitive hourly rates depending on experience, 40 hrs/week.
Engagement via W2 or personal LLC only. No third parties.
Fully remote, aligned to EST.
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What you’ll be doing
• Building predictive and statistical models for real business use cases
• Working with large structured and semi structured datasets
• Partnering with data engineering to productionize models
• Designing experiments and validating model performance
• Translating business problems into analytical solutions
• Supporting model monitoring and continuous improvement
What we’re looking for
• Strong experience as a Data Scientist in production environments
• Solid Python and SQL
• Experience with ML frameworks (scikit-learn, XGBoost, PyTorch, etc.)
• Strong statistics and modeling fundamentals
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• Comfortable working with messy, real-world data
• Experience in agile, delivery-focused teams
Nice to have
• Cloud experience (AWS / Azure / GCP)
• Feature engineering and model deployment exposure
• Experience supporting product or growth teams
• Basic MLOps familiarity
Why this role
• Applied data science, not research
• Real business impact
• Flexible engagement model, extensions likely
• Opportunity to work with experienced data teams
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