IQVIA

IQVIA

Scientifique des données associé, confidentialité et désidentification/ Associate Data Scientist, Privacy and

Ottawa, Ontario, Canada · Part-time

Sponsorship not specified$50k-$83kDetected 1 day ago
PythonSQLData AnalysisData ScienceStatisticsRecruitingHRISClinical TrialsClinical ResearchResearchCommunicationProblem SolvingMentoringPublic Speaking

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odds of building a lasting career here

42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds (Level I)39
Fits your clock70

Thin sponsorship signal and lottery-bound (~15% per draw). A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.

Lottery odds assume a STEM candidate.

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Employer immigration record

from this employer's Department of Labor filings

Green-card follow-through: 100%

Of 7 labor certifications old enough to have been used, 0 expired without the employer filing the next step. Median time from filing to decision: 493 days.100% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.

DOL often reclassifies their job titles

The Department of Labor assigned a different occupation than this employer requested on 54% of its 28 wage determinations — against a 18% norm. The occupation sets the prevailing wage floor.Reclassification is routine and can reflect genuinely hybrid roles; it is not by itself evidence of underpayment.

Files H-1B transfers

4 transfer filings in the last year, covering 4 workers. Median labor-condition decision: 316 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

Community outcomes

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About the role

  • The ability to gain information from various sources and apply them to new problems is important for success and growth within this role.
  • IQVIA se consacre à accélérer le développement et la commercialisation de traitements médicaux innovants pour aider à améliorer les résultats des patients et la santé de la population dans le monde entier.

Responsibilities

  • Develop expertise in statistical disclosure control and de-identification regulations and standards
  • Validate data received from clients and prepare it for re-identification risk measurement
  • Help prepare materials (presentations, reports, etc.) in support of delivery of data de-identification solutions to technical and business audiences
  • workday_recruiting@iqvia.com, so that IQVIA can support your participation in the recruitment process.
  • IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud.

Requirements

  • 1 year of relevant work experience
  • Experience performing data analysis, data exploration and data modeling (using SQL and R/Python)
  • Knowledge of Privacy Analytics methodology
  • Experience anonymizing data and preparing data for anonymization
  • Experience supporting and tracking of multiple projects at various stages of development
  • Ability to present work and explain findings to senior team members
  • A post graduate degree and research experience
  • Experience working in a professional services organization, such as consulting
  • Knowledge of statistical disclosure control or privacy laws
  • All information and credentials submitted in your application must be truthful and complete.
  • B.Sc en mathématiques, statistiques, informatique, physique ou domaine connexe
  • 1 an d'expérience professionnelle pertinente
  • Compétences solides en mathématiques et statistiques
  • Expérience en analyse de données, exploration de données et modélisation de données (utilisant SQL et R/Python)
  • Connaissance de la méthodologie Privacy Analytics
  • Expérience en anonymisation des données et préparation des données pour l'anonymisation
  • Expérience de soutien et de suivi de plusieurs projets à divers stades de développement
  • Capacité à présenter le travail et à expliquer les résultats aux membres seniors de l'équipe
  • Excellentes compétences en communication verbale et écrite
  • Excellentes compétences en résolution de problèmes

Nice to have

  • Développer une expertise en contrôle de la divulgation statistique et en réglementations et normes de désidentification
  • Valider les données reçues des clients et les préparer pour la mesure des risques de réidentification
  • Pour en savoir plus, visitez https://jobs.iqvia.com

Skills

  • Having good communication skills to present information in a clear and concise manner to internal stakeholders will be needed.

Compensation

  • La fourchette salariale de base potentielle pour ce poste, annualisée, est de $49,700.00 - $82,800.00.
  • The potential base pay range for this role, when annualized, is $49,700.00 - $82,800.00.
  • The actual base pay offered may vary based on a number of factors including job-related qualifications such as knowledge, skills, education, and experience; location; and/or schedule (full or part-time).
  • Dependent on the position offered, incentive plans, bonuses, and/or other forms of compensation may be offered, in addition to a range of health and wellness and/or other benefits.

Benefits

  • Support qualitative and quantitative re-identification risk analysis for a variety of clients in and outside of the healthcare domain, and support documentation of the results of these analyses in client-facing reports
  • Experience working with health, financial, or other consumer data

Company info

  • As part of the Data Privacy Solutions team, the primary focus of the Associate Data Scientist will be to assist with client engagements focusing on de-identification and re-identification risk measurement of client data.

This listing is sourced directly from IQVIA's careers page and normalized into a canonical job model.