Forward Deployment Lead

ConcertAI•Moorestown, NJ

12d

About The Position

The Forward Deployment Product Lead (FDPL) serves as the critical bridge between our clinical trial technology platform and the operational needs of large pharmaceutical sponsors. This role blends product strategy, applied AI/ML expertise, and hands-on deployment leadership. The FDPL ensures that complex clinical, operational, and data workflows are deeply understood, translated into actionable product requirements, and successfully enabled through modern software, AI capabilities (including LLMs), and scalable data solutions. You will work directly with customer teams to guide solution design, workflow optimization, model-driven features, and data integration strategies. Internally, you will bring structured feedback from real-world deployments to shape our AI-enabled roadmap and ensure the platform delivers measurable impact across global clinical development.

Requirements

Minimum Bachelor's degree; Advanced degree preferred
5-10+ years of experience in clinical trial software, life sciences technology, AI-enabled SaaS, or data/analytics-driven product delivery.
Strong understanding of clinical development workflows (study startup, data management, monitoring, medical review, trial operations).
Demonstrated success leading complex enterprise software deployments with large pharma or CROs.
Experience working with AI/ML products, LLMs, data science teams, or analytics-driven workflow tools.
Ability to translate ambiguous user needs into crisp product requirements and model-supported workflows.
Strong communication skills and comfort engaging senior R D, digital, and data/AI stakeholders.
Travel to customer sites is required and expected to be 75%
Ideally located in the NY / NJ / CT .

Nice To Haves

Familiarity with NLP/LLM applications in regulated environments (document automation, knowledge retrieval, summarization, labeling).
Experience in machine learning-driven risk assessment, operational analytics, or predictive modeling for clinical trials.
Understanding of GxP, validation requirements, model monitoring, and change control for AI-enabled systems.
Background in product management, technical consulting, or data science applied to life sciences

Responsibilities

Act as the primary product lead for strategic pharma accounts, ensuring successful deployment, adoption, and value realization of both core platform capabilities and AI-driven features.
Conduct deep discovery with clinical operations, biometrics, medical, regulatory, and data management stakeholders to identify high-value use cases for automation, prediction, and generative AI support.
Lead cross-functional squads (engineering, data science, solutions, customer success) through implementation cycles that include workflow design, model integration, and iterative refinement.
Serve as the customer's strategic partner during trial- and program-level deployments; anticipate change management needs associated with AI feature introduction and ensure responsible, compliant use of advanced technologies.
Translate clinical workflows and customer pain points into requirements for AI-enabled capabilities (e.g., protocol summarization, automated monitoring insights, predictive enrollment modeling, anomaly detection, document intelligence).
Collaborate with Product Management and Data Science teams to evaluate feasibility, design user interactions, and prioritize model improvements based on real-world usage.
Partner with customers to validate model outputs, collect feedback, and assess performance in regulated settings, ensuring transparency and alignment with GxP expectations.
Champion opportunities where LLMs, machine learning, and data engineering can streamline processes such as study startup, query management, risk-based monitoring, medical review, or vendor oversight.
Lead requirements for structured and unstructured data flows between the platform and clinical systems (EDC, CTMS, eCOA, imaging, safety, medical writing tools, and document repositories).
Work closely with data engineering and ML teams to define training data needs, metadata standards, and validation criteria for AI-enabled features.
Help customers understand and operationalize insights derived from predictive models, natural language processing workflows, or large-scale data analytics.
Guide customers through adoption of AI-driven capabilities, including communication plans, training materials, workflow redesign, and impact assessments.
Develop customer-facing resources—workflow maps, configuration guides, validation documentation, and best-practice playbooks for AI feature usage.
Drive stakeholder alignment across study leadership, IT, data science groups, and CRO partners to ensure consistent understanding and responsible implementation.