Data Science Project Manager
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Job Description
Data Science Project Manager coordinating machine learning projects, MLOps readiness, and cross-functional delivery at RealPage. Improving data science processes and production workflows.
Responsibilities:
- 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.
Requirements:
- 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.
Benefits:
- 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.
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