Data Science Project Manager

. Manage the day-to-day execution of multiple data science and machine learning projects.

Posted 9/30/2026full-timeRemote • Texas • United StatesMid-LevelSenior💰 $94,700 - $161,300 per yearWebsite

Key responsibilities & impact

Manage the day-to-day execution of multiple data science and machine learning projects.
Translate business problems and strategic priorities into project scopes, objectives, milestones, deliverables, and success criteria.
Maintain project plans, backlogs, roadmaps, timelines, dependencies, risks, decisions, and action items.
Coordinate work across data scientists, data engineers, software engineers, ML engineers, product managers, and business stakeholders.
Facilitate sprint planning, standups, retrospectives, status reviews, demos, and project working sessions.
Identify blockers, drive resolution, and escalate decisions when necessary.
Ensure project documentation, requirements, assumptions, and decisions are current and accessible.
Provide regular reporting on progress, risks, dependencies, and expected outcomes.
Track requirements for reproducibility, versioning, testing, deployment, monitoring, and documentation.
Coordinate model release readiness, production handoffs, and post-deployment follow-up.
Ensure teams consider data quality, model performance, drift, reliability, security, and support ownership.
Help establish processes for model promotion, retraining, monitoring, incident response, and model retirement.
Create scalable operating processes, templates, dashboards, meeting cadences, and documentation standards for the Data Science organization.
Identify recurring delivery issues and recommend improvements to processes, tools, roles, or decision-making.

What you’ll need

5+ years of experience in technical project management or related delivery role.
Demonstrated experience managing data science, machine learning, analytics, or other highly technical projects.
Experience coordinating cross-functional teams like Data Science, Engineering, and Product functions.
Ability to understand technical discussions involving data pipelines, experimentation, model evaluation, APIs, deployment, and production support.
Strong project management skills, including planning, prioritization, dependency management, risk management, and status reporting.
Excellent written and verbal communication skills.
Ability to operate effectively with ambiguity and bring structure to complex, evolving work.
Strong attention to detail and follow-through.
Experience working in Agile, Scrum, Kanban, or similar delivery environments.
Hands-on experience using AI in workflows effectively and responsibly.
Strong understanding of the data science and machine learning lifecycle.
Experience with MLOps, ML platforms, or production machine learning systems.
Familiarity with model deployment, model registries, experiment tracking, CI/CD, workflow orchestration, model monitoring, data quality monitoring, and retraining processes.
Familiarity with responsible AI, model risk management, privacy, explainability, bias, or regulatory considerations.
Technical background in data science, computer science, engineering, analytics, mathematics, or a related discipline.

Comp & perks

Health, dental, and vision insurance.
Retirement savings plan with company match.
Paid time off and holidays.
Professional development opportunities.
Performance-based bonus based on position.
Additional rewards, including annual bonus and sales incentives, depending on the applicable plan, role, and individual performance.