Altimetrik Poland is a digital enablement company. We deliver bite-size outcomes to enterprises and start-ups from all industries in an agile way to help them scale and accelerate their businesses. We are unique in Poland's IT market. Our differentiators are an innovation-first approach, a strong focus on core development, and an ability to attack the challenging and complex problems of the biggest companies in the world.
We are looking for a highly experienced Project Manager with 15+ years of expertise in delivering complex Data & AI initiatives within the Life Sciences and Healthcare sector. The ideal candidate brings a rare combination of strong project governance, deep life science domain knowledge, and hands-on familiarity with data engineering, machine learning, and AI platforms. You will lead end-to-end delivery of data transformation and AI adoption programs for pharmaceutical, biotech, CRO, and health-tech organizations — on time, within scope, and with measurable business impact.
◆ Key Responsibilities
Project Planning & Governance
● Lead end-to-end project management of Data & AI programs — from initiation and discovery through delivery, hypercare, and transition to BAU.
● Define project scope, objectives, milestones, and success criteria in collaboration with business sponsors, data engineering, and AI teams.
● Develop comprehensive project plans, resource plans, RACI matrices, risk registers, and communication plans for multi-stream data initiatives.
● Establish and enforce project governance frameworks: steering committee cadence, RAID logs, change control processes, and escalation protocols.
● Manage project budgets from $500K to $10M+; track actuals vs. forecasts and deliver cost-efficiency initiatives.
● Drive delivery using hybrid methodologies — Agile (Scrum/Kanban) for data development sprints and Waterfall for regulatory and validation workstreams.
Data & AI Program Delivery
● Oversee delivery of data platform modernization programs: data lake/lakehouse migrations, cloud data warehouse implementations (Databricks, Snowflake, AWS, Azure), and data pipeline development.
● Manage AI/ML project lifecycles — from use case identification, data readiness assessment, model development, and validation through production deployment and monitoring.
● Coordinate delivery of Generative AI and LLM-based solutions for life science applications: regulatory document automation, clinical trial analytics, pharmacovigilance signal detection, and drug discovery support.
● Govern data migration programs including legacy system decommissioning, data cleansing, ETL pipeline build, and data reconciliation.
● Ensure traceability and auditability of all data and AI deliverables in line with GxP, 21 CFR Part 11, and CSV/CSA requirements.
Stakeholder Management & Communication
● Serve as the primary point of contact for C-suite sponsors, VP-level business owners, and external vendors across all project workstreams.
● Facilitate executive steering committee meetings, project working groups, and cross-functional workshops with pharma, biotech, and CRO stakeholders.
● Translate complex technical data and AI concepts into clear business language for non-technical senior leaders and regulatory audiences.
● Manage relationships with third-party technology vendors, system integrators, and cloud providers (AWS, Azure, GCP, Databricks, Snowflake).
● Prepare and present project status reports, executive dashboards, and milestone updates to internal and client leadership.
Risk, Quality & Change Management
● Proactively identify, assess, and mitigate project risks specific to data quality, AI model performance, regulatory compliance, and system integration.
● Lead change management activities to drive adoption of new data platforms and AI tools across clinical, regulatory, and commercial teams.
● Manage scope creep, competing priorities, and resource constraints across concurrent data and AI workstreams.
● Conduct post-project retrospectives, lessons learned sessions, and contribute to PMO best practices and delivery playbooks.
● Ensure all project deliverables meet quality standards through structured review, UAT coordination, and sign-off processes.
Team Leadership & Vendor Management
● Lead cross-functional delivery teams of 10–50+ members: data engineers, ML engineers, data scientists, business analysts, QA leads, and domain SMEs.
● Manage third-party vendor contracts, SLAs, and performance — including offshore/nearshore delivery teams.
● Coach and mentor junior project managers and business analysts; contribute to PMO capability development.
● Foster a collaborative, high-performance delivery culture aligned with agile and continuous improvement principles.
◆ Required Skills & Qualifications
Project Management Expertise
● 15+ years of project management experience, with 5+ years leading Data & AI programs in Life Sciences or Healthcare.
● Proven track record delivering large-scale data platform, analytics, ML, and AI projects ($1M–$5M+ budgets, 12–36 month durations).
● Expert proficiency in Agile (Scrum, SAFe, Kanban) and Waterfall/hybrid methodologies; experienced in mixed-methodology program delivery.
● Strong command of project management tools: JIRA, Confluence, MS Project, Smartsheet, Azure DevOps, or equivalent.
● Experienced with PMO governance: portfolio reporting, resource management, dependency tracking, and benefits realization.
Life Sciences Domain Knowledge
● 15+ years of experience in pharma, biotech, CRO, medtech, or health IT — with deep understanding of drug development lifecycle.
● Hands-on experience managing GxP-regulated projects: CSV/CSA, IQ/OQ/PQ validation, audit trail requirements, and 21 CFR Part 11 compliance.
● Familiarity with life science platforms: Veeva Vault, Medidata Rave, Oracle Clinical, SAS, and laboratory information management systems (LIMS).
Data & AI Literacy
● Strong working knowledge of modern data architectures: data lakehouse, cloud data warehouse, ETL/ELT pipelines, and data mesh.
● Familiarity with key platforms: Databricks, Snowflake, AWS (S3, Glue, Redshift, SageMaker), Azure (Synapse, ADF, Azure ML), or GCP.
● Understanding of ML/AI lifecycle: data preparation, model development, validation, deployment, monitoring, and MLOps.
● Awareness of Generative AI, LLMs, and RAG pipelines — able to facilitate technical discussions and scope AI initiatives with engineering teams.
Leadership & Communication
● Exceptional stakeholder management skills — credible and confident with C-suite executives, regulatory bodies, and technical delivery teams.
● Strong written and verbal communication; ability to produce executive presentations, status reports, and regulatory documentation.
● Demonstrated experience leading globally distributed, cross-functional teams across time zones and cultures.
● Skilled negotiator and conflict resolver — able to align competing priorities across business, IT, and compliance functions.