Actuarial Associate

ZIAF KHAN

**** ****** ***, *****, *** York *****

917-***-**** ****.*.****@*****.*** linkedin.com/in/ziafkhan ACTUARIAL EXAMS

• Exam P Passed March 2017

• Exam FM Passed June 2017

• Exam MFE Passed November 2017

• Exam SRM Credited via Applied Statistics VEE

• Exam LTAM Passed October 2018

• Exam STAM Passed February 2020

• Exam PA Sitting June 2020

• VEEs: Corporate Finance, Economics, Applied Statistics Completed PROFESSIONAL EXPERIENCE

Buck Secaucus, NJ

Actuarial Associate June 2018 – Present

• Develop actuarial assumption sets and valuations using ProVal for defined benefit pension plans

• Perform data reconciliation, including client communication to verify anomalous data

• Utilize ProVal output for analyses such as gain/loss, sensitivity, and experience studies

• Prepare funding reports, and year-end disclosure reports for client financials on accounting bases

• Write monthly spot rate analyses for team and client: highlights trends and facilitates seasonal projects

• Produce benefit calculations and relative value forms for participants with varying plan provisions

• Adapt to irregular projects such as alive audits or lump sum windows

• Volunteered as a summer intern mentor in 2019:

o Oversaw intern responsibilities and gave exposure to different aspects of our business o Instructed and provided feedback to improve quality of work and time efficiency EDUCATION

Binghamton University, State University of New York Binghamton, NY Bachelor of Science: Actuarial Science Graduated May 2018 ACADEMIC PROJECTS

Binghamton University Data Science and Analytics (BUDSA) Binghamton, NY Datathon 2018 February 2018

• Led 4-person team in BUDSA Datathon 2018 and won 1 st

place in category: Housing Data

o Delegated tasks to team members to complete presentation within the 10-hour time limit o Cleaned raw data provided by local business Live in Bing to a more usable form with Excel o Formulated a model to predict rent prices using linear regression on the cleaned dataset in R o Graphed predictive models using ggplot2 and created a slideshow using PowerPoint Statistics Paper, Predicting Unemployment Binghamton, NY Linear Regression February 2018 – May 2018

• Worked in a three-man team for three months using US county census data to create a predictive model for unemployment; presented our work and submitted our paper with citation and codework

• Used Bayesian information criterion with stepwise elimination for model selection, Box-Cox transformation on response to attain homoskedasticity SKILLS

Programming: Proficient with R, Microsoft Office suite, ProVal, LaTeX. Basic use of HTML, Python Analysis: Linear Regression, Time Series, Fourier Transform, Credibility, ANOVA

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