IT Project Manager (Data Science)
First seen by Alion on Nov 13, 2026.
Experience Level: 6–8 Years
Engagement Type: Client-Facing / Embedded Consultant
Role OverviewWe are looking for a proactive, execution-driven IT Project Manager to embed directly within our client’s Data Science team. Acting as a trusted delivery partner on-site/remotely with the client, you will be responsible for driving day-to-day activities, structuring workflows, and keeping complex data science and machine learning initiatives on track. You will bridge the gap between the client’s technical data science leads, IT infrastructure teams, and business stakeholders to ensure smooth, predictable delivery from experimentation to production.
Key ResponsibilitiesClient Delivery & Task Execution: Take ownership of day-to-day project execution within the client’s Data Science team—breaking down high-level analytical goals into structured workstreams, sprint backlogs, and actionable tasks.
Agile & Workflow Facilitation: Run daily stand-ups, sprint planning, and retrospectives tailored to the client’s environment, balancing exploratory data science R&D with firm delivery milestones.
Cross-Team & Client Stakeholder Alignment: Act as the central point of coordination across the client’s Data Scientists, Data Engineers, IT/Infrastructure teams, and business sponsors to resolve dependencies, data access bottlenecks, and environment requirements.
Risk, Scope & Blocker Management: Proactively surface delivery risks, technical blockers, or scope creep early, driving timely resolution and keeping client leadership informed with clear mitigation plans.
Status Reporting & Governance: Provide transparent, structured reporting on sprint velocity, milestone progress, and resource utilization to both client stakeholders and internal account leadership. Ensure all deliverables align with the client’s data security, privacy, and IT governance standards.
Process Improvement: Introduce practical, lightweight project management discipline (e.g., Agile/Kanban or CRISP-DM) that accelerates team throughput without disrupting the data scientists' research cadence.
Experience: 6–8 years of experience in IT Project Management, Technical Project Management, or Delivery Management, including 2–3+ years working directly with Data Science, Machine Learning, AI, or Analytics teams.
Client-Facing Consulting Mindset: Strong consultative presence with proven experience managing external client stakeholders, navigating client organizational dynamics, and building trust quickly within embedded teams.
Hands-On Task Driver: Demonstrated track record of taking initiative, driving daily tasks to closure, and keeping multi-disciplinary teams accountable without relying solely on formal authority.
Data Science & IT Fluency: Solid working understanding of the end-to-end data science lifecycle (data ingestion, feature engineering, model training, validation, and MLOps/deployment) to effectively challenge timelines and communicate with technical leads.
Methodology & Tooling: Strong command of Agile, Scrum, and Kanban practices, along with proficiency in enterprise project tools (e.g., Jira, Azure DevOps, Confluence, Asana) and familiarity with cloud platforms (AWS, GCP, or Azure).
Communication: Crisp, executive-ready communication skills capable of translating complex technical progress and blockers into clear business terms for client leadership.
Certifications such as PMP, PMI-ACP, CSM (Certified ScrumMaster), or SAFe.
Prior experience working in an IT consulting, professional services, or managed delivery environment.
Experience managing initiatives involving Generative AI / LLMs, predictive modeling, or large-scale data platform migrations.
Skills
Client-Server Environment, relationships with external stakeholders, Timeline Management, AWS, Liaising With Sponsors, Client Implementation, Technological Proficiency, Service Integrity, Safe Environment, Client Management, Risk Mitigation, Accountability Management, Data Science, IT Service Delivery, Generative AI Innovation, Research and Development, and Innovation Management, Sprint methodology