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Data Science / Analytics 🏢 Full Time ⭐️ Verified

Senior Applied Statistician

ApexStat Labs
Cambridge, MA
Salary Estimate
USD 110.000 – USD 160.000
Posting Time
6 Mei 2026
Deadline
6 Mei 2027

Job description

Join ApexStat Labs, a leading applied statistics firm focused on turning data into actionable product and policy insights. We’re seeking a Senior Applied Statistician to lead complex analyses, build predictive models, and guide decision-makers across cross-functional teams.

In this role, you’ll work on real-world datasets, design experiments, and deliver interpretable insights that drive strategy and innovation.

Responsibility

  • Lead end-to-end applied statistical analyses across product and operations lifecycles.
  • Develop, validate, and deploy predictive and causal models using Python, R, and SQL.
  • Design experiments (A/B/n tests), perform power analyses, and interpret results for stakeholders.
  • Collaborate with data engineering to ensure data quality, reproducibility, and scalable pipelines.
  • Create compelling data visualizations and dashboards to communicate insights to non-technical audiences.
  • Translate business questions into statistical frameworks and actionable recommendations.
  • Mentor junior analysts and promote statistical best practices across teams.
  • Document methodologies and ensure reproducible research with version control.

Qualification

  • MS or PhD in Statistics, Biostatistics, Mathematics, Economics, or related field.
  • 3+ years of applied statistics experience in industry or research settings.
  • Proficiency in Python (pandas, scikit-learn) and R; strong SQL skills.
  • Experience with experimental design, causal inference, and predictive modeling.
  • Strong communication and storytelling skills; ability to present complex results clearly.
  • Familiarity with data visualization tools (Tableau, Power BI) is a plus.
  • Ability to work in a fast-paced environment and manage multiple projects.

Required Skills

applied statistics statistics biostatistics experimental design A/B testing predictive modeling Python R SQL data visualization causal inference machine learning

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