Data Scientist

  • 35051
  • Non-Life - Data Science
  • |
  • New York, United States
  • |
  • Apr 12, 2019
Reinsurance
RESPONSIBILITIES
  • Gain understanding of existing tools by supporting a variety of ongoing talent analytics work.
  • Assist in replacing and displacing spreadsheet-based approaches wherever feasible.
  • Assist in improving benchmarking efforts, whether in data collection or analysis.
  • Produce documentation and assist in training efforts for broader roll-outs as needed.
  • Apply predictive analytics tools to both automate existing processes and produce new insights.
  • Improve reporting and visualization of analysis results for both internal and external audiences.
  • Suggest innovative ways to collect and compile data from existing resources to produce new products and services, as well as greater insights into client needs and operations.
  • Continue to learn industry best practices around data manipulation, aggregation, and analysis to support the Culture & Engagement teams’ goal of leading the industry in talent data science.
  • Efficiently deliver quality products and services to clients or colleagues on time, and ensure work product consistently meets expectations for accuracy, process, and reasonability.
  • Continuously develop and apply improved procedures to increase accuracy and efficiency, and alert team members to deficiencies in data, processes, and results.
  • Work effectively with colleagues across a variety of disciplines, provide constructive feedback, and support collaboration within the company.
  • Continue to develop industry, technical, and programming knowledge to stay ahead of developments in the profession.
QUALIFICATIONS
  • BS/BA Degree or equivalent in mathematics, statistics, computer science, economics, finance, or other related analytical fields. MS/MA preferred.
  • 3 - 5 years’ experience.
  • Talent industry experience preferred, but not required.
  • Experience communicating technical concepts to less-technical people.
  • Financial Services experience preferred but not required.
  • Strong abilities with Microsoft Excel, Word, PowerPoint, and Access.
  • Strong experience with SQL and R and/or Python.
  • Skills in techniques such as multiple regression, logistic regression, decision trees, and random forest.
  • Ability to operate effectively through challenging or ambiguous situations.
  • Good communication skills, both oral and in writing.
  • Good organization and time management skills; ability to work independently.
  • Close attention to detail.
  • Strong commitment to teamwork, excellence, and personal/professional growth.