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ジョブゲッチャー

Customer Success Data Analyst

Jobgether

Remote ・ フルタイム

最初に応募しよう

経験
2年以上
給料
求人情報
1
投稿済み
2時間前
作業モード
在宅勤務
教育
Master's degree in Life Sciences or related field
再開する
応募必須

仕事内容

Overview

This position is offered on behalf of a partner company based in Canada seeking a Customer Success Data Analyst. The role enables contributing to scientific advancements by transforming complex biological data into actionable insights that support influential discoveries.

The successful candidate will act as a bridge between sophisticated data analysis and client interactions, ensuring accurate, timely, and impactful delivery of data solutions. Situated at the convergence of science, technology, and customer engagement, this job supports researchers in informed decision-making through clear visualizations and rigorous analyses.

This opportunity suits a professional skilled in technical analysis, communication, and collaborative problem solving, aimed at enhancing customer satisfaction while advancing cutting-edge biological research methods.

Responsibilities

  • Ensure customers receive precise and prompt data following defined generation and quality control protocols.
  • Prepare finalized data reports and develop clear visualizations to aid scientific evaluation.
  • Create and maintain standardized reporting workflows tailored to customer projects.
  • Assist with customer onboarding, provide continuous support for data platform usage, and resolve inquiries or issues.
  • Act as primary customer liaison to understand research objectives, collect feedback, and guarantee project success.
  • Convert customer feedback into actionable suggestions for product and platform enhancements.
  • Participate in testing data tools and platforms to find defects and propose improvements.
  • Collaborate with data, product, and development teams to resolve issues and optimize analytics workflows.
  • Design customized data pipelines for internal processes, quality control, collaborations, and unique customer specifications.
  • Simultaneously handle multiple customer projects, ensuring quality and adherence to deadlines.
  • Engage in ongoing improvements to enhance the customer experience and analytical capabilities.

Requirements

  • Master’s degree in Life Sciences emphasizing data analysis or a closely related discipline.
  • At least two years of experience with data analysis programming, preferably using Python.
  • Background in bioinformatics, computational biology or data science is advantageous.
  • Strong knowledge of statistical methods including principal component analysis (PCA).
  • Minimum one year of experience in roles involving customer interaction or service orientation.
  • Experience supporting technical customers or technical teams is a positive asset.
  • Effective verbal and written communication skills to clearly convey complex concepts.
  • Ability to collaborate productively within diverse, cross-functional groups.
  • Excellent organizational aptitude with skill in prioritizing, managing deadlines, and delivering independently.
  • A proactive approach to identifying improvement opportunities and driving projects to completion.
  • Comfortable adapting in a dynamic and evolving work environment.

Benefits

  • Competitive full-time compensation commensurate with experience, skills, and geography.
  • Fully remote role available for candidates in the United States or Canada, ideally aligned with Eastern Time Zone hours.
  • Flexible work arrangements including occasional onsite collaboration up to 25% as required.
  • Chance to contribute meaningfully to innovative scientific research and advanced biological data solutions.
  • A collaborative and supportive culture engaging scientists, engineers, data professionals, and problem solvers.
  • Professional development opportunities through exposure to novel technologies, scientific applications, and customer workflows.
  • Direct influence on how researchers utilize data to accelerate their discoveries.
  • A workplace environment focused on innovation, ongoing learning, and continuous process improvement.

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