• Analyze integrated and extensive datasets to extract value affecting business decisions • Work collaboratively with key business stakeholders to identify areas of value and deliver insights • Interpret and analyze data from multiple sources including claims, provider, member, and encounters data • Identify and assess business impact of trends • Develop, maintain, and troubleshoot complex scripts and reports using SQL, Excel, or other analytics tools • Contribute to planning and execution of large-scale projects with limited direction • Assist in designing, testing, and implementing process enhancements and opportunities for automation • Identify and perform root-cause analysis of data irregularities and present findings • Manage multiple tasks and data review processes with limited supervision • Demonstrate ownership over projects and understand business value of tasks • Apply expertise in quantitative analysis and data mining to understand customer interaction with analytic products • Communicate findings to non-technical business partners and engage with customers to gather requirements.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a highly motivated Bioinformatics Analyst to join the research group of Dr. Robert Burk at Albert Einstein College of Medicine. This role is central to translating large-scale sequencing datasets into rigorous, publication-ready results with direct relevance to chronic disease, cancer prevention, and infectious disease research. - Pipeline development and leadership: Design, implement, document, and maintain end-to-end NGS pipelines for microbiome and HPV genomics applications. - Large-scale data processing: Perform robust QC, read processing, reference alignment, taxonomic and functional profiling, strain-level analyses, and reproducible reporting for cohort-scale datasets. - Clinical and epidemiologic integration: Harmonize sequencing outputs with clinical and epidemiologic metadata, perform data cleaning and validation, and generate analysis-ready tables. - Statistical and computational analysis: Conduct and interpret statistical analyses using R and/or Python, including microbiome-specific methods and epidemiologic modeling. - Project leadership and communication: Lead defined analysis workstreams, set realistic milestones, communicate risks and dependencies early, and present progress and results in lab meetings and to collaborators. - Troubleshooting and optimization: Diagnose and resolve complex issues, improve robustness, scalability, and runtime efficiency on shared compute environments. - Reproducibility and best practices: Use version control, structured documentation, and reproducible execution practices. - Scientific contribution: Propose fresh analytic ideas, evaluate new tools and methods, and contribute to interpretation and narrative framing of findings. - Manuscripts and grants: Contribute figures, methods text, and analyses for manuscripts and grant applications. Qualifications - Bachelor’s degree in bioinformatics, computational biology, biostatistics, epidemiology, computer science, or a related field; Master’s or PhD preferred. - Strong programming ability in R and/or Python, plus comfort working in a Unix/Linux environment. - Demonstrated experience processing and analyzing NGS data, including building or extending pipelines. - Solid understanding of basic statistical concepts and the ability to translate scientific questions into appropriate analyses. - Track record of independent problem solving, attention to detail, and producing reliable, well-documented outputs. - Strong communication skills and a collaborative mindset for working with interdisciplinary teams. Requirements - Experience in microbiome bioinformatics and/or viral genomics with familiarity with phylogenetics. - Experience working with protected clinical or epidemiologic data and with best practices for data security and governance. - Experience analyzing large cohorts and handling confounding, batch effects, and complex study designs. - Comfort translating analyses into clear figures, methods, and results text suitable for high-impact manuscripts. - Practical experience with bioinformatics work and a proactive attitude towards learning and applying new methods and technologies. Benefits - In compliance with NYC's Pay Transparency Act, the annual base salary range for this position is listed below. - Maximum Salary Range: USD $65,000.00/Yr.

• Collaborate with SMEs across the organisation to define and implement data governance strategies to ensure that master data is consistent, secure and accurate globally. • Play a key role in defining requirements, design, testing and implementation of new processes and system enhancements to support master data. • Assist in leading process standardisation, data governance, cleansing, maintenance and quality improvement efforts in line with data strategy goals. • Conduct root cause analysis on master data inconsistencies/errors and drive corrective actions. • Conduct regular audits of data against established rule sets to ensure consistency and accuracy of information, collaborating with suppliers and business partners to address any data discrepancies. • Provide day-to-day support of the master data governance process to the business user community. • Identify and document existing processes, workflows, gaps, and risks; summarise insights and communicate implications for internal and external stakeholders. • Serve as the vital link between business, the MDM centres of excellence and IT while providing knowledge and advice in own areas of expertise. • Supports the process of identifying, describing, and realising opportunities for the development and use of global master data across information systems. • Support system development activities by assisting in requirements gathering, the drafting of test scripts, test execution, etc.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Real World Data Analyst role is a high-impact, intellectually challenging position that requires a diverse set of analytical and technical skills. It offers a unique opportunity for professional growth through access to an unprecedented volume of high-quality, real-world Electronic Health Record (EHR) data. In this role, you will tackle meaningful healthcare challenges using an EHR database encompassing more than 125 million patients—and growing—helping drive insights that can ultimately improve patient care. This position reports to the Senior Director of Research Services. - Translate study protocols and statistical analysis plans (SAPs) into reproducible analytical code, executing end-to-end research studies from data extraction and wrangling through statistical analysis and results generation using SQL, R, or Python within agreed timelines. - Review and interpret study protocols and SAPs, ensuring analytical approaches are implemented accurately and consistently with study objectives. - Develop robust data pipelines for cohort building, data cleaning, feature engineering, and preparation of analytical datasets using large-scale real-world data sources. - Conduct rigorous quality control (QC) of code, datasets, and statistical outputs, including verification of model assumptions, reproducibility of results, and validation of analytical approaches. - Proactively identify and communicate potential data issues, methodological concerns, or deviations from study protocols early in the research process, recommending solutions and mitigation strategies. - Collaborate closely with biostatisticians and researchers to support the development, refinement, and review of statistical analysis plans and analytical methods. - Interpret analytical results and contribute to the synthesis of findings, ensuring outputs are scientifically sound and aligned with study objectives. - Partner with life sciences customers and internal teams to execute high-quality analyses that support peer-reviewed publications, regulatory submissions, and scientific research. - Provide clear, proactive communication on study progress, analytical challenges, and timelines, ensuring stakeholders remain informed and aligned. - Work closely with cross-functional teams across Truveta to investigate data sources, validate data integrity, and support scientific credibility of research outputs. - Deliver feedback to internal teams based on research experiences and customer needs to help inform improvements to Truveta’s data platform and research capabilities. Qualifications - Undergraduate or graduate degree in biostatistics, epidemiology, clinical research, data science, clinical informatics, or a related field. - 3+ years of experience conducting analyses using large healthcare datasets (e.g., electronic health records or other real-world data sources). - Strong proficiency in SQL and at least one statistical programming language (R or Python) for data wrangling, analysis, and reproducible research workflows. - Experience translating research protocols or statistical analysis plans into executable analytical code. - Experience performing rigorous quality control of analytical datasets, code, and statistical outputs. - Demonstrated experience working with complex healthcare data involving millions of patient records. - Experience collaborating with research teams or customers to deliver high-quality scientific analyses. - Strong problem-solving skills and the ability to identify data or methodological issues early and communicate them effectively. - Excellent written and verbal communication skills, including the ability to clearly explain analytical approaches and findings to technical and non-technical stakeholders. - Ability to learn quickly and adapt in a dynamic, fast-paced environment. - Willingness to learn new analytical tools, programming languages, and proprietary platforms used to analyze real-world data. Preferred Qualifications - Experience applying causal inference methods to observational healthcare data, including approaches such as propensity score matching/weighting, inverse probability weighting, marginal structural models, or instrumental variable analysis. - Familiarity with study designs used to estimate causal effects in real-world data, such as target trial emulation, difference-in-differences, regression discontinuity, or interrupted time series. - Experience evaluating confounding, selection bias, and other threats to causal validity in observational studies. Benefits - Interesting and meaningful work for every career stage - Great benefits package - Comprehensive benefits with strong medical, dental and vision insurance plans - 401K plan - Professional development & training opportunities for continuous learning - Work/life autonomy via flexible work hours and flexible paid time off - Generous parental leave - Regular team activities (virtual and in-person)