Core Responsibilities

• Own end-to-end delivery for assigned client projects across clinical data management, analytics, data science, and information management.
• Lead sprint planning, effort estimation, resource allocation, risk/issue management, and status reporting; ensure on-time and on-budget outcomes.
• Perform hands-on solutioning and execution (e.g., gathering business requirements, writing data pipelines, standards mapping, model building, dashboarding etc.) while guiding junior team members.
• Collaborate with client stakeholders (ClinOps, Biometrics, Safety, Medical Affairs, R&D IT) to align scope, requirements, acceptance criteria, and success metrics.
• Ensure compliance with GxP, 21 CFR Part 11, data privacy, and Axtria’s GenAI/InfoSec policies when applicable.
• Contribute to capability building: develop PoCs, POVs, case studies, delivery playbooks, and reusable accelerators for the Clinical Solutions COE.
• Support pre-sales: solution outlines, effort/rate cards, proposal inputs, demos, and bid defenses.
• Report on portfolio health: utilization, margins/P&L drivers, delivery risks, and upcoming opportunities; escalate proactively.

Domain Experience

• Strong understanding of clinical trial lifecycle (Phase I–IV) and core functions: Clinical Operations, Biometrics/Statistical Programming, Drug Safety/Pharmacovigilance, and Medical Affairs.
• Hands-on work with clinical systems/data sources: EDC (e.g., Medidata Rave, Veeva Vault CDMS), CTMS, IRT/RTSM, ePRO/eCOA, LIMS/Labs, and real-world data (EHR/EMR).
• Working knowledge of data standards: CDISC (CDASH, SDTM, ADaM), HL7/FHIR, LOINC; familiarity with audit readiness and submission workflows.
• Experience delivering clinical insights: enrollment tracking, protocol compliance, KRI/KPI reporting, adverse event analysis, site performance, and patient journey analytics.
• Working knowledge of AI/ML/NLP and GenAI concepts and their use cases in clinical domain with at least basic understanding of how these advanced analytics solutions work, what are their advantages and their challenges

Technical Skills

• Data engineering & analytics: Python/PySpark, SQL; orchestration on Databricks
• Cloud & data platforms: Azure/AWS/GCP; data warehousing (e.g., Snowflake/Azure SQL); governance, lineage, and quality frameworks.
• Visualization: Power BI, Tableau, or Spotfire for clinical dashboards and executive reporting.
• Modeling & AI: Familiarity with classical ML (classification, regression, time-series, clustering), applied NLP and LLM/GenAI concepts (RAG, prompt design, evaluation).
• Project tools & methods: Agile/Scrum, JIRA/Confluence, MS Project/Smartsheet; strong documentation and estimation practices.

Soft Skills

• Client-facing communication with the ability to translate business needs into technical deliverables and measurable outcomes.
• People leadership: mentoring, performance feedback, delegation, and conflict resolution.
• Structured problem-solving, ownership mindset, and proactive risk management.
• Stakeholder management across onshore/offshore teams; ability to thrive in fast-paced, multi-project environments.

Qualifications

• Bachelor’s or Master’s in Engineering, Computer Science, Statistics, Data Science, Life Sciences, or related field.
• At least 14 years of relevant experience across clinical data management/analytics/data science/data engineering; prior people-management experience preferred (4 years).
• Demonstrated track record delivering complex clinical data/analytics projects with high billability and quality.
• Certifications are a plus: SAS, AWS/Azure, PMP/CSM, or similar.