Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. Responsibilities: - Collaborate with cross-functional teams including medical oncologists, research scientists, and engineers to develop and maintain clinical data models. - Translate customer needs and product requests into key concept definitions and business logic for complex models. - Facilitate integration of data model into workflows, applications, and data deliveries. - Structure and normalize data from a variety of sources, including Tempus curated data, EHR integrations, and lab systems. - Develop and maintain knowledge bases for clinical concepts. - Create and execute validation plans in conjunction with SMEs for new models and disease types. - Proactively monitor and support quality assurance and process improvement initiatives. - Monitor performance of production processes and recommend areas for improvement. Qualifications: - 5+ years of oncology data modeling experience. - Experience working with real world data from various sources (e.g., curation workflows, EHRs, lab systems, claims, research datasets). - Experience working with modern ELT tools such as DBT to maintain high volume, high velocity data warehouses - Experience working with standard medical terminologies (e.g., SNOMED CT, RxNorm, LOINC, ICD-9/10). Nice-to-haves: - Clinical background (MD/DO, PharmD, PA, NP, RN, etc.). - Familiarity with next-generation sequencing data. - Experience with real world data analysis in Python or R. - Participation in professional organizations (e.g., OHDSI, AMIA). #LI-BL1 We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

• Own warehouse platform architecture. Design and evolve the systems that support storage, transformation, governance, and access across clinical datasets. • Build scalable data infrastructure. Develop a secure and compliant warehouse infrastructure. • Healthcare-specific data modeling: Design and build clinical data models inside a scalable warehouse solution. • Design warehouse optimization strategies. Create data models and storage approaches that balance performance, scalability, and maintainability. • Establish data governance frameworks. Implement controls that ensure data quality, consistency, security, and auditability. • Enable downstream analytics and AI workloads. Build infrastructure that supports reporting, evidence generation, and machine learning applications. • Lead cross-functional technical initiatives. Partner with engineering, analytics, and product teams to align platform capabilities with business priorities. • Improve platform observability and reliability. Build monitoring, alerting, and operational tooling that ensures trust in critical data systems.

• Own data pipeline development. Build and maintain reliable pipelines that ingest, transform, and deliver healthcare data across the organization. • Design warehouse data models. Create scalable schemas and data structures that support analytics, reporting, and evidence generation. • Lead data transformation strategy. Establish frameworks and standards that improve consistency, maintainability, and performance. • Ensure data quality and trust. Implement validation, reconciliation, monitoring, and alerting processes across critical datasets. • Support clinical data integration initiatives. Help incorporate new clinical data sources into the warehouse ecosystem. • Optimize warehouse performance. Improve query efficiency, storage utilization, and processing costs as data volumes grow. • Partner with stakeholders on data requirements. Translate business questions into durable data solutions and trusted datasets. • Improve operational excellence. Develop tooling and processes that increase reliability and reduce manual intervention. • Mentor data engineers and analysts. Share best practices and elevate technical standards across the data organization.

Role Description The Data Recording Specialist will provide technical support to strengthen pedigree development, performance recording, molecular characterization, and data management systems for endangered indigenous livestock breeds under the project. - Assist in reviewing and assessing existing data recording systems for target breeds (Bactrian camel, Sarabi cattle, Mahabadi goat, Markhoz goat). - Support the development of practical recommendations to strengthen breed registration, animal identification, pedigree recording, and performance data collection systems. - Assist in preparing technical specifications and TOR/LOA inputs for contracting laboratories to conduct molecular analysis. - Provide technical support in the evaluation of laboratory offers, ensuring compliance with required standards. - Support development of sampling strategies, sample size determination, and data management procedures for molecular analysis. - Assist in pedigree analysis, including estimation of inbreeding coefficients and identification of breeding strategies. - Support identifying procurement needs and prepare technical specifications for equipment related to milk collection/analysis and wool/mohair processing centers. - Assist in developing and delivering training materials and workshops for technicians and community-level recorders. - Support preparation of technical reports summarizing findings, recommendations, and progress achieved. - Perform other related duties as required by the project team. Qualifications - Advanced university degree in animal breeding and genetics, Molecular Genetics, Animal Biotechnology, Animal Science, Veterinary Genetics, or a closely related field. - At least five (5) years of progressively relevant professional experience in livestock genetics data recording and analysis. - Working knowledge (level C) of English. - National of I.R. Iran or resident of the country with a valid work permit. Requirements - Strong knowledge of molecular genetics applications in livestock parentage verification and breed characterization. - Familiarity with molecular marker technologies and interpretation of parentage testing results. - Experience with DNA sample collection and laboratory coordination for molecular analysis. - Knowledge of international standards for animal genetic resources conservation. - Ability to advise on sampling strategies and data management for genetic studies. - Good training, reporting, communication, and stakeholder coordination skills. - Knowledge of indigenous livestock breeds and production systems in Northwest Iran is an asset. Benefits - Home-based position. - Follow the UN workweek (from Sunday to Thursday) and holiday schedule.