Data Scientist (Contract | Hybrid | Operations Analytics)

Job Description

About

We're looking for an experienced Data Scientist to help shape operational strategy through statistical analysis, experimentation, and data-driven decision-making. You'll work with large datasets, evaluate tradeoffs between cost, efficiency, and customer experience, and turn complex business questions into actionable recommendations. This role is focused on solving business problems, not simply building dashboards or producing reports.

What You'll Do

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• Analyze large datasets from multiple sources to identify opportunities for operational improvement and cost optimization.
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• Apply statistical modeling, experimentation, and causal inference to evaluate business decisions and quantify potential impact.
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• Assess tradeoffs across financial performance, operational efficiency, and customer experience metrics.
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• Independently structure ambiguous analytical problems, validate assumptions, and develop appropriate analytical approaches.
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• Partner with Finance, Operations, Product, and other stakeholders to translate findings into strategic recommendations.
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• Build reusable analytical solutions and clearly communicate methodology, findings, and business impact.
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What We're Looking For

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• Strong foundation in statistics, predictive modeling, experimentation, and causal inference.
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• Experience working with large datasets, data transformation, and integrating multiple data sources.
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• Proficiency in SQL and/or Python for data analysis and statistical modeling.
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• Ability to independently manage complex analytical problems from initial discovery through recommendations.
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• Experience evaluating multiple business metrics and quantifying financial, operational, or customer impact.
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• Strong communication skills, including the ability to explain analytical methods and influence business decisions.
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• Preferred: Experience in operations analytics, workforce planning, contact center analytics, or causal inference methods such as propensity score matching and difference-in-differences.
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