Project Manager – Data Science (Contract, Onsite – Southern California)

Los Angeles, CA · Contract · Experienced · 50 - 50 USD Hourly

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Walk us through a data science or machine learning project you personally managed from initiation through to production. Please describe the business problem, the size and composition of the team (data scientists, ML/data engineers, analysts), your specific responsibilities across the model lifecycle (data acquisition, experimentation, validation, deployment, monitoring), and the measurable outcome the project delivered.

Data science delivery is rarely linear — experiments fail, timelines shift, and models don't always reach the accuracy the business hoped for. Tell us about a time a data or ML initiative you were managing went off-track. How did you diagnose what was happening, what trade-offs did you weigh between scope, timeline, and model performance, and how did you communicate the situation to non-technical stakeholders while keeping the team motivated?

This is a contract engagement that is fully onsite in Southern California, five days per week — there is no hybrid or remote option. Please confirm your current location (city) and your ability to work onsite daily for the duration of the contract, describe your familiarity with the Southern California area, and share any context we should know about your availability, notice period, and comfort working as a contractor rather than a permanent employee.

Describe how you plan, track, and govern data science projects specifically. Which delivery methodology do you favor for research-heavy work (Agile, Scrum, Kanban, or a hybrid), what tools do you use to manage the pipeline and reporting (e.g. Jira, Azure DevOps, MLflow, dashboards), and how do you build realistic roadmaps and manage scope when a meaningful part of the work is experimental and its outcome is uncertain at the outset?

Senior data science projects live or die by stakeholder alignment and risk management. Tell us about the most complex data initiative you have owned — one with significant executive visibility, budget, or competing priorities. How did you manage stakeholder expectations across the business, data, and engineering functions, how did you identify and mitigate the biggest risks (data quality, privacy/compliance, adoption, or technical feasibility), and what was the ultimate impact of your leadership on the outcome?

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