Full-Time
Senior Data Analyst 3
Posted on 8/13/2026
Elsevier
10,001+ employees
Global STM publisher of journals.
No salary listed
Bengaluru, Karnataka, India
In Person
 Data & Analytics (1) |
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- Significant experience in analytics engineering, data transformation, or modern business intelligence engineering roles.
- Advanced SQL expertise, including performance optimization and complex transformation logic.
- Experience building and maintaining data models using dbt in production environments.
- Experience using Python for data processing and automation.
- Experience working with cloud data warehouses, preferably Snowflake.
- Strong understanding of dimensional modeling, semantic layers, and metric standardization.
- Experience with AWS or equivalent cloud technologies.
- Ability to translate business requirements into technical data models and engage with stakeholders.
- Experience implementing data testing frameworks and maintaining high data quality standards.
- Clear written documentation skills, particularly for metric definition logic and lineage.
- Experience with Tableau or Power BI.
- Design, build, and maintain scalable, modular data models using dbt to support analytics and reporting across Technology Infrastructure and Operations.
- Translate business and reporting requirements into robust, reusable data models with clearly defined metrics and logic.
- Implement data transformation best practices, including testing, documentation, and version control.
- Collaborate with data engineers to ensure reliable ingestion, schema evolution, and data quality controls.
- Partner with analysts to enable high-quality dashboarding through well-structured, analytics-ready datasets.
- Perform data profiling, validation, and root cause analysis to improve data quality and reliability.
- Lead analytics initiatives end-to-end, including discovery, requirements, iterative development, validation, release, maintenance, and user support.
- Lead technical design discussions and contribute to architectural decisions in the analytics stack.
- Experience working with Snowflake.
- Experience with Tableau or Power BI.
Elsevier provides scientific, medical, and technical content and analytics to researchers, educators, clinicians, and institutions worldwide through journals, books, databases, and digital platforms. It operates by offering access to peer-reviewed articles and professional literature via online platforms, along with tools for discovery, citation tracking, research management, and clinical decision support. Unlike other publishers, Elsevier combines a broad portfolio of STM content with integrated analytics and workflow tools, serving researchers, healthcare professionals, and academic institutions on a global scale. Its goal is to enable the advancement of knowledge and improve health outcomes by delivering reliable, accessible information and data-driven solutions across the research lifecycle.
Company Size
10,001+
Company Stage
N/A
Total Funding
N/A
Headquarters
Amsterdam, Netherlands
Founded
1880
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Simplify's Take
What believers are saying
- LeapSpace launched Writing Coach and Claim Radar on June 25, 2026, strengthening retention.
- ClinicalKey Student added StudyFinder AI on June 25, 2026, capturing medical-school AI demand.
- Elsevier signed its first CzechELib open-access deal on July 2, 2026, widening reach.
What critics are saying
- GPS Air trial starts December 7, 2026; a verdict could punish editorial liability.
- Judge Bell sanctioned Elsevier $10,000 on August 2026, signaling weak courtroom discipline.
- Consortia like CzechELib force APC concessions, squeezing hybrid-journal pricing power and margins.
What makes Elsevier unique
- Elsevier owns trusted STM content, Scopus data, and workflow AI across research and care.
- LeapSpace and ClinicalKey AI embed evidence directly into drafting and clinical workflows.
- Wellsheet acquisition on June 3, 2026 adds EHR context inside clinician decision-making.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
401(k) Company Match
Unlimited Paid Time Off
Flexible Work Hours
Paid Vacation
Paid Sick Leave
Paid Holidays
Sabbatical Leave
Employee Assistance Program
Short-and-Long Term Disability
Life Insurance
Accidental Death Insurance
Wellness Program
Family Planning Benefits
Fertility Treatment Support
Health Savings Account/Flexible Spending Account
Company News
Defamation suit demanding Elsevier retract paper heads closer to trial. * Slashdot By EditorDavidAug 23, 2026, 12:33 am103 ptsTrending Retraction Watch reports: A trial date has been set in a $1 billion defamation case against Elsevier that alleges the company published what plaintiffs say was a manipulated study about an air purifying technology over objections from peer reviewers. The case has already cost Elsevier a $10,000 sanction from a... Engineering & Technology Read Article Share Article * email * x.com * facebook * pocket * reddit * tumblr * linkedin * pinterest Tech News Tube is a real time news feed of the latest technology news headlines. Follow all of the top tech sites in one place, on the web or your mobile device.
Development of a dynamic Risk Prediction tool for identifying individuals at high risk of Cardiovascular diseases: A post-hoc analysis of the Multi-Ethnic Study of Atherosclerosis. Affiliations & Notes Article Info Highlights. A Bayesian joint model was applied in a large multi-ethnic cohort with long-term follow-up to develop a dynamic CVD risk prediction tool incorporating the history of key risk factors. The model showed improved discrimination, calibration, accuracy, and reclassification performance, with the greatest gains observed when more extensive longitudinal histories were included. A web-based dynamic risk prediction tool was developed to provide individualized estimates of 10-year CVD risk. This study is among the first to translate joint modelling methodology into a clinically applicable risk prediction tool. Abstract. Background and aims. Cardiovascular disease (CVD) remain the leading cause of mortality and disability. Most risk prediction tools rely on risk factors measured at a single time point, ignoring changes in biomarkers over time. Elsevier Inc. aimed to develop and validate a dynamic CVD risk prediction tool using joint modelling that incorporates repeated measurements of cardiovascular risk factors. Methods. Elsevier Inc. analysed 6,637 participants aged 45-84 years from the Multi-Ethnic Study of Atherosclerosis. A Bayesian joint model was used to simultaneously analyse longitudinal risk factor trajectories and time to CVD. Repeated measurements of total cholesterol, systolic blood pressure, HDL-C, and use of lipid-lowering and antihypertensive medications were incorporated as time-varying covariates. Predictive performance was assessed using discrimination, calibration, overall accuracy, and reclassification measures. Results. The joint model identified significant temporal trends, including declining cholesterol (−1.14 mg/dL/year) and increasing HDL-C (0.43 mg/dL/year), while systolic blood pressure remained stable. All risk factors were independently associated with CVD risk. Compared with a conventional model based on a single baseline measurement, the joint model demonstrated improved discrimination, calibration, overall accuracy, and reclassification, with gains becoming more pronounced as longer longitudinal histories were incorporated. A web-based application was developed to support individualized dynamic CVD risk prediction. Conclusions. Incorporating longitudinal risk factor trajectories provides more accurate CVD risk estimates than conventional models. This approach improves risk stratification and may enhance clinical decision-making for preventive interventions. To its knowledge, this is among the first studies to translate joint modelling methodology into a clinically applicable CVD risk prediction tool. Graphical abstract. Get full text access. References. Naghavi, M. ∙ Kyu, H.H. ∙ A, B... Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023 The Lancet. 2025;, 0 Farzadfar, F. Cardiovascular disease risk prediction models: challenges and perspectives Lancet Glob Health. 2019; 7 :e1288-e1289 D'Agostino, R.B. ∙ Vasan, R.S. ∙ Pencina, M.J... General cardiovascular risk profile for use in primary care: The Framingham heart study Circulation. 2008; 117 :743-753 Goff, D.C. ∙ Lloyd-Jones, D.M. ∙ Bennett, G... ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American college of cardiology/American heart association task force on practice guidelines J Am Coll Cardiol 2014. 2013; 63 :2935-2959 SCORE2 risk prediction algorithms: New models to estimate 10-year risk of cardiovascular disease in Europe Eur Heart J. 2021; 42 :2439-2454 Lindbohm, J.V. ∙ Sipilä, P.N. ∙ Mars, N... Association between change in cardiovascular risk scores and future cardiovascular disease: analyses of data from the Whitehall II longitudinal, prospective cohort study Lancet Digit Health. 2021; 3 :e434-e444 McClelland, R.L. ∙ Jorgensen, N.W. ∙ Budoff, M... 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) with Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study) J Am Coll Cardiol. 2015; 66 :1643-1653 Chamnan, P. ∙ Simmons, R.K. ∙ Sharp, S.J... Repeat Cardiovascular Risk Assessment after Four Years: Is There Improvement in Risk Prediction? PLoS One. 2016; 11, e0147417 Mach, F. ∙ Koskinas, K.C. ∙ Roeters van Lennep, J.E... Focused Update of the 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Developed by the task force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and the European Atherosclerosis Society (EAS) Eur Heart J. 2025; 2025 published online Aug 29 Oulhaj, A. ∙ Aziz, F. ∙ Suliman, A... Joint longitudinal and time-to-event modelling compared with standard Cox modelling in patients with type 2 diabetes with and without established cardiovascular disease: An analysis of the EXSCEL trial Diabetes Obes Metab. 2023; 25 :1261-1270 Stevens, D. ∙ Lane, D.A. ∙ Harrison, S.L... Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review BMC Medical Research Methodology. 2021; 21 (1):1-24 Hayes, A.J. ∙ Leal, J. ∙ Gray, A.M... UKPDS Outcomes Model 2: A new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year united kingdom prospective diabetes Study: UKPDS 82 Diabetologia. 2013; 56 :1925-1933 Rizopoulos, D. Joint models for longitudinal and time-to-event data: With applications in R Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. 2012; 1-257 Arnett, D.K. ∙ Blumenthal, R.S. ∙ Albert, M.A... ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines Circulation. 2019; 140 :e596-646 Figures (7). Article metrics. Metric data currently unavailable
Elsevier's LeapSpace wins best generative AI solution at CODiE Awards. 23 July 2026 Elsevier's AI-powered research platform, LeapSpace, has been named Best Generative AI Solution at the 2026 CODiE Awards. The CODiE Awards, established in 1986, are among the technology industry's longest-running peer-reviewed awards programmes, recognising companies, products and leaders across software, AI, data, cybersecurity and digital learning. LeapSpace is Elsevier's research-focused AI workspace, designed to support both academic and corporate researchers. The platform combines publisher-neutral scholarly content with AI-powered tools including traceable citations, Trust Cards, Claim Radar and Writing Coach to help researchers explore evidence, validate outputs and accelerate research workflows. Elsevier says the platform has been developed in collaboration with customers to ensure it continues to meet the evolving needs of the research community. Judy Verses, President, Academic and Government, Elsevier, said: "This CODiE Award is a testament to what makes LeapSpace different. Researchers need more than generic AI. They need trusted evidence, clear citations, transparent reasoning, and control over how insights are interpreted and used. LeapSpace was built with customers' needs at its core, and this recognition reflects the dedication of our colleagues who developed the solution and the customers and research communities who continue to shape it with us." The platform has been designed to address growing demand for AI tools that are built on trusted scientific content, provide transparency in how outputs are generated and enable researchers to maintain oversight of their work. Dr. Sunil Kumar Satpathy, Head of the Central Library, National Institute of Technology Raipur, India, said: "LeapSpace helps researchers overcome intense time pressure, information overload, and missed insights. It boosts efficiency in literature reviews, supports mission-oriented research, and helps identify research gaps and trends. Our researchers appreciate that LeapSpace provides comprehensive, peer-reviewed content and responsible AI in one place." The CODiE Awards use a peer-reviewed judging process combining expert evaluation with community voting to recognise excellence across a range of technology sectors.
Elsevier signs first CzechELib open access agreement. 2 July 2026 Elsevier has signed its first agreement with the CzechELib consortium, led by National Library of Technology of the Czech Republic. The five-year agreement enables eligible corresponding authors at participating CzechELib institutions to publish open access in selected Elsevier hybrid journals without paying article publishing charges (APCs). The publisher said the agreement marks an important milestone in its relationship with the Czech research community and supports the country's transition towards open science. "The agreement enables researchers to publish in high-quality journals while removing financial barriers associated with open access," said Petr Očko, Director of the National Library of Technology. Under the agreement, eligible corresponding authors affiliated with participating CzechELib institutions will be able to publish open access in selected hybrid journals, with APCs covered through the agreement. Elsevier said the partnership is intended to support wider dissemination of Czech research, simplify the publishing process for researchers and institutions, and contribute to national open science priorities. William Reubens said: "We are proud to establish our first agreement with CzechELib and to begin this partnership as a trusted partner to the Czech research community. This agreement provides researchers with a clear and supported route to publishing open access in leading journals, helping to increase the visibility and impact of their work while removing financial and administrative barriers." Jiří Jirát added: "This first agreement with Elsevier represents an important step forward for the Czech research community. By working together as trusted partners, we are enabling greater access to open access publishing and supporting researchers in sharing their work more widely on a global stage. During the first few months of the agreement, authors from all types of institutions have already shown great interest in publishing under it."
Elsevier has expanded LeapSpace, its research-grade AI workspace, with new agentic capabilities including Writing Coach, Claim Radar and Compare Tables. The platform draws on over 20 million peer-reviewed articles and books from Elsevier and 1,000 content partners, plus 100 million scientific records from Scopus. Writing Coach provides a private space for drafting and refining research work with AI assistance, whilst Claim Radar verifies evidence by assessing how claims align with published literature. Compare Tables automates literature comparison work by extracting evidence into structured formats. LeapSpace is already used by thousands of researchers globally, with 97% reporting time savings and over half saving more than 50% of research time. The platform also now supports Word document uploads and reference exports, addressing key user requests.