Data Science Project Manager

This role will serve as a connective layer across Data Science, Data Engineering, Software Engineering, Product, Operations, and business stakeholders. The Technical Project Manager will bring structure, visibility, and execution discipline to our Data Science Lifecycle—from problem definition and data preparation through model development, deployment, monitoring, and continuous improvement.

The ideal candidate has direct experience managing data science or machine learning projects and is comfortable working in a highly technical, fast-moving environment. This is a tactical, execution-oriented role for someone who enjoys turning complex work into clear plans, coordinating dependencies, removing blockers, and ensuring that projects progress toward measurable business outcomes.

Experience in Prop Tech, Fin Tech, real estate technology, financial services, or other data-intensive SaS environments is a plus. Experience with MLOps and production machine learning workflows is also highly desirable. Experienced effectively using AI within their own workflow to accelerate/amplify reach and impact. Experience working with sensitive, confidential, personally identifiable, or financially relevant data is a plus.

• Data Science Project Execution Manage the day-to-day execution of multiple data science and machine learning projects.
• Help translate business problems and strategic priorities into clear 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 early, drive resolution, and elevate decisions when necessary.
• Ensure that project documentation, requirements, assumptions, and decisions are current and accessible.
• Provide concise, regular reporting on progress, risks, dependencies, and expected outcomes.
• MLOps and Production Readiness Help Track requirements for reproducibility, versioning, testing, deployment, monitoring, and documentation.
• Help 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 repeatable processes for model promotion, retraining, monitoring, incident response, and model retirement.
• Process Improvement and Operating Rhythm Help create lightweight, scalable operating processes for the Data Science organization.
• Help develop templates, dashboards, meeting cadences, and documentation standards that improve team effectiveness.
• Help identify recurring delivery issues and recommend improvements to processes, tools, roles, or decision-making.

• 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, & 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 on how to use 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.

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