Senior Data Scientist
Hire FeedAbout the role
Role: Senior Data Scientist (Remote)
Location: Remote (Work from Anywhere)
Job Type: Contract
Payout: $60-$70 / hr
Role Overview:
We are hiring for one of our clients, seeking Data Science and Analytics Experts to build enterprise-scale evaluation tasks for AI systems operating in Fortune 500 enterprise data and analytics contexts. The workflows are calibrated to the data scale, model complexity, and business-critical stakes of Fortune 500 and large public company data operations. Contributors will design enterprise data science scenarios, draft reference outputs, and write rubrics that capture how senior Fortune 500 data leaders think.
Key Responsibilities:
- Construct enterprise data science scenarios spanning large-scale predictive modeling, multi-stakeholder analytics governance, and complex data infrastructure decisions at Fortune 500 accounts.
- Build analytics tasks across Fortune 500 machine learning model development, enterprise data pipelines, business intelligence at scale, experimentation/causal inference, and data strategy.
- Develop data and ML Ops scenarios involving tools such as Snowflake, Databricks, Python/R, SQL, Tableau/Power BI, and enterprise ML platforms (SageMaker, Vertex AI, MLflow) in Fortune 500 stacks.
- Apply enterprise data science methodologies (statistical rigor, A/B testing frameworks, model validation, ML Ops best practices) and produce reference analyses, model documentation, and executive-level insights.
- Author rubrics that distinguish authentic enterprise data science judgment from generic textbook or tutorial-level recall.
Required Skills & Qualifications:
- 5+ years working as a data scientist, analytics leader, or ML engineer at a Fortune 500 technology or enterprise organization or inside a Fortune 500 data/analytics organization.
- Direct ownership of Fortune 500 data products, analytics initiatives, or machine learning systems in production.
- Fluency in enterprise data science tooling and methodologies for Fortune 500 contexts.
- Experience with Fortune 500 data governance, privacy compliance, and cross-functional analytics leadership.
- Proficiency in Python/R, SQL, and enterprise BI tools (Tableau/Power BI).
- Familiarity with cloud data platforms (Snowflake, Databricks) and MLOps tools (SageMaker, Vertex AI, MLflow).
- Understanding of statistical rigor, A/B testing frameworks, and model validation in enterprise settings.
- Ability to produce executive-level insights and model documentation for Fortune 500 stakeholders.
More About the Opportunity:
This role offers a unique opportunity to work with a global leader in the technology industry, contributing to the evaluation of AI systems that power Fortune 500 enterprise operations. The work directly shapes how enterprise leaders assess and deploy AI solutions at scale.
Equal Opportunity Employer:
We hire based on skills and expertise. All qualified candidates are welcome regardless of background, experience, or prior employment history. Applications are reviewed solely on demonstrated technical ability and qualifications.
Apply Now!
Responsibilities
- Construct enterprise data science scenarios spanning large-scale predictive modeling and multi-stakeholder analytics governance.
- Build analytics tasks across Fortune 500 machine learning model development and enterprise data pipelines.
- Develop data and ML Ops scenarios involving tools such as Snowflake, Databricks, Python/R, SQL, Tableau/Power BI.
- Apply enterprise data science methodologies and produce reference analyses and model documentation.
- Author rubrics that distinguish authentic enterprise data science judgment.
Qualifications
- 5+ years working as a data scientist, analytics leader, or ML engineer at a Fortune 500 organization.
- Direct ownership of Fortune 500 data products or machine learning systems in production.
- Fluency in enterprise data science tooling and methodologies.
- Experience with data governance, privacy compliance, and cross-functional analytics leadership.
- Proficiency in Python/R, SQL, and enterprise BI tools.
- Familiarity with cloud data platforms and MLOps tools.
- Understanding of statistical rigor and model validation in enterprise settings.
Skills mentioned
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