Jobs at Trinity Life Sciences

Owns end-to-end recruiting for experienced professionals and leaders, with emphasis on Analytics, Insights, and Technology talent. Responsibilities include intake discussions, sourcing, passive candidate engagement, screening, interview coordination, assessments, offer negotiation, pipeline management, talent-market analysis, recruiting metrics, stakeholder advising, and candidate experience. Partners with executives, hiring managers, HR, subject-matter experts, and external recruiting providers to fill specialized and leadership roles.

Owns end-to-end recruiting for experienced professionals, managers, and senior leaders across Commercial Strategy and TGaS. Responsibilities include intake, sourcing, talent mapping, screening, structured interviews, stakeholder advising, offer negotiation, pipeline management, recruiting analytics, and candidate engagement. The role requires life sciences consulting or commercial recruiting expertise, senior stakeholder partnership, market intelligence, and data-driven hiring recommendations.

Conducts statistical, econometric, optimization, and machine learning analyses to solve business problems, particularly in the pharmaceutical domain. Develops models and simulation tools, performs advanced data analysis, collaborates with clients and internal teams, manages project implementation, and ensures stakeholder alignment, timelines, and quality. Requires hands-on experience with Python, R, SAS, PySpark, SQL, and BigQuery.

Data Science Interns analyze healthcare and real-world data using SQL, Python, R, and other programming languages. They develop standardized code modules, support consulting projects, evaluate secondary data sources, and assist with project timelines and client engagements. The role includes training, mentorship, and exposure to life sciences commercialization, patient-level data, AI tools, and strategic analytics. This is a 9-week, in-person summer internship requiring collaboration, strong analytical and communication skills, and proficiency in Excel and PowerPoint.

Provides remote Level 2 desktop and service desk support, diagnosing and resolving hardware, software, endpoint, conference room, mobile device, and user issues. Manages tickets, assets, escalations, documentation, security baselines, BitLocker, device compliance, and SOC 2 practices. Supports Microsoft Teams rooms, identifies recurring problems, recommends improvements, and collaborates with IT teams while maintaining strong customer communication and SLA adherence.

Supports life sciences consulting engagements through secondary and primary research, quantitative analysis of market dynamics, sales, volume, and pricing, respondent recruitment, expert interviews, and development of findings presentations. Consultants work across multiple project teams, collaborate with internal and external stakeholders, and build therapeutic-area knowledge while strengthening analytical, communication, and leadership skills.

Own and improve product strategy and operations across product development, roadmapping, resourcing, go-to-market planning, market research, sales enablement, launches, performance metrics, and stakeholder training. The role partners with Product, AI, Data Science, Marketing, Finance, HR, executives, sellers, and clients to translate analysis into strategic recommendations and accelerate Digital and AI product commercialization.

Conduct business research, industry interviews, market and cost analyses, financial modeling, and client presentations. Associates support strategy consulting, opportunity assessment, valuation, and custom assignments under supervision. The role involves information collection, quantitative analysis, client interaction, potentially demanding and irregular hours, and possible international travel. Candidates need at least one year of life sciences or pharmaceutical industry experience, strong analytical and communication skills, financial understanding, and a bachelor’s degree from a top institute.

Designs, develops, deploys, and maintains predictive models for life sciences marketing analytics. Responsibilities include analyzing marketing effectiveness, sales engagement, next-best-action, demand planning, customer segmentation, and test-and-control problems. Applies statistical, econometric, and optimization methods to structured and unstructured data, communicates actionable findings, and supports end-to-end client projects.

Designs, develops, and operationalizes enterprise knowledge platforms and AI solutions. Leads multi-LLM orchestration, RAG pipelines, knowledge graphs, SharePoint and Power Platform development, Power BI reporting, governance, and cross-functional stakeholder work to deliver scalable KM solutions.

Analyze benchmark, survey, and client organizational data to identify trends, outliers, and actionable insights. Create accurate dashboards, charts, reports, and client deliverables using Excel, PowerPoint, Power BI, and automation tools. Support survey design, benchmarking analyses, product development, and process improvements while communicating methods and findings clearly. Collaborate with team members, manage projects independently, and translate complex quantitative and qualitative data into business-friendly recommendations.

Conduct data analysis and develop statistical, econometric, optimization, and machine learning models for pharmaceutical business problems. Collaborate with clients, leadership, and internal teams to define requirements, manage projects, build simulation tools, and deliver high-quality results on schedule. Use Python, R, SAS, PySpark, SQL, and BigQuery while managing internal and external stakeholders.

Build and deploy generative AI solutions for biopharma and life sciences clients, including Copilots, RAG pipelines, agentic workflows, and data integrations. Own projects from discovery through production, rapidly prototype and iterate with clients, and improve reusable AI components. Mentor and guide a small team of AI engineers through pairing, code reviews, onboarding, and technical coaching. Translate ambiguous client needs into production-quality systems while contributing to product development and knowledge-sharing.

Analyze structured datasets, identify trends, and communicate business insights through reports, presentations, dashboards, and visualizations. Contribute to analytics and benchmarking product initiatives, integrate technology and analytics, refine outputs for accuracy, and collaborate with peers and managers. The role requires strong Excel, Power BI, statistical, communication, and problem-solving skills, with preferred experience in competitor analysis, Salesforce reporting, survey platforms, automation, and life sciences.

Design, implement, support, and troubleshoot enterprise system and data integrations across cloud platforms. Develop containerized integrations, contribute to data mapping and metadata, maintain technical documentation, and improve performance and reliability. Collaborate with engineering, platform, data, and business teams to deliver integration solutions and support deployments.

Lead design and delivery of cloud-native, AI-first full‑stack applications. Architect scalable serverless and containerized services, implement pixel‑perfect UIs, build Django/GraphQL APIs, integrate LLMs and multi‑modal AI, enforce testing/CI/CD, observability, security, and mentor engineering teams while collaborating across product, design, and data.

Lead and manage DevSecOps/security engineering teams; oversee CI/CD and cloud security (AWS/Azure/GCP); implement vulnerability scanning, incident response, threat analysis, configuration management, API/container security, and compliance (HIPAA, SOC2, GDPR, CCPA). Communicate security posture to technical and non-technical stakeholders and continuously monitor security controls across IT frameworks.

Design, implement, fine-tune, and deploy ML models and GenAI/LLM applications (summarization, sentiment, content generation). Build data pipelines, research GenAI frameworks (LangChain, LlamaIndex), optimize models for performance and scalability, and collaborate with cross-functional teams to integrate AI into business processes.

Research, prototype, and integrate LLMs and NLP solutions into business use cases. Experiment with and fine-tune ML/DL models, optimize performance, build data pipelines, and collaborate with cross-functional teams to deploy LLM-based applications.

Manage client engagements and serve as client point of contact. Lead forecast model builds, develop forecasting best practices, create client-ready forecasts and materials, implement quality control, coordinate internal/external timelines, provide on-site client support when needed, communicate project status, support strategic initiatives, and train junior forecasting team members.