SUMMARY

A statistics graduate student with * years of academic experience and 5 years of work experience in the telecommunication industry seeking an entry-level position as a statistical analyst

ACTUARIAL PROFESSIONAL EXAMS

Passed Probability/1 Nov, 2011

Sitting for Financial Mathematics/2 Dec, 2013

EDUCATION

University of Nevada, Reno, US

• MS Mathematics (Statistics Major). GPA: 3.83 and expected graduation date Dec, 2013

Kwame Nkrumah University of Science & Technology, Kumasi, Ghana

• BS Electrical/Electronic Engineering Jun, 2006

COMPUTER SKILLS

• Software: MS Office

• Prog. Language: C, C++, and Java

• Stat applications: R, SAS, and Minitab

• Web: HTML, CSS, and Javascript

• Database: MYSQL, Vertica, MS Access

• Machine Learning: WEKA, R, and Knime

• Scripts: Shell, Batch, PHP, Python, Matlab

TECHNICAL SKILLS

• Statistics: Time Series Analysis (ARIMA, GARCH, Spectral Analysis), Generalized Linear Models (Multiple Linear Regression, Log-linear regression, Logistic regression, Probit), Robust Models (Quantile Regression), Predictive Modeling, Hypothesis Testing, Principal Components & Factor Analysis, Cluster and Discriminant Analysis, Canonical Correlation, Stochastic Modeling, and Experimental Design.

• Machine Learning: Support Vector Machine, Artificial Neural Network, Genetic Algorithm, Decision Trees, Bayesian Learning, Reinforcement Learning, and Ensemble Learning.

EXPERIENCE

Property Radar, Truckee, CA.

Statistical Analyst (Intern) (May 2013 – Aug 2013)

I was responsible for:

• Developing a statistical model for the project ‘The Contribution Of Mix And Price On The Median Sales Price Of California Residential Real Estate’ for which I successfully completed.

• Assisting California Association of Realtors on a project to develop a Case Shiller’s Home Price Model for the company.

• Providing statistical insight to the director of economics research for the weekly data reports.

University Of Nevada, Reno, NV.

Teaching Assistant (Aug 2011 – Present)

This required

• Conducting discussion classes to go over mathematical concepts and problems.

• Holding office hours for students to help them with assignments and the understanding of course materials.

• Organizing quizzes, proctoring exams and grading them.

Airtel,Accra,Ghana. Network Operations Supervisor: (Jan 2010 – Aug 2011)

I was involved in the following:

• Supervising a team of 7 engineers to monitor, perform first line troubleshooting and resolve or escalate major network faults to higher-level engineers.

• Resolving customer complaints on poor mobile reception and connectivity.

• Writing scripts to collect network data for analysis.

MTN, Accra, Ghana.

Switch O&M Engineer (Nov 2006 – Jan 2010)

As an operation and maintenance engineer I was:

• First-line troubleshooting and resolving mobile switching faults (signaling and hardware).

• Taking backups and restoring corrupted switches.

• Assisting Ericsson engineers in network upgrade and work orders.

• Performing daily health checks on all network nodes.

MASTER THESIS

Estimation of the Ratio of Variances for Two Independent Normal Populations: The estimation of the variance ratio involves the construction of confidence interval using an F-distribution. Due to the characteristics of the distribution (asymmetric, asymptotic and positive), implementing the standard equal tail confidence interval is not efficient as portrayed under most practical sense. Other methods such as test inversion and the unequal division of the tail areas into chosen proportion were explored and found out to be better than the traditional method. Predictions of the optimized tail proportions were also presented. The relevance of this estimation is evident in the analysis of risk between two portfolios that exhibits a normal distribution such as the log return of stock prices.

INTERNSHIP PROJECT

The Contribution of Mix and Price on the Median Sales Price of California Residential Real Estate: During a rapid change in the housing market such as a downturn or a recovery, median prices becomes a poor measure of the actual value of the home. Breaking the median price down into mix (amount due to the features of the real estate) and price (amount caused by external economic factors), we arrive at a more accurate picture of the change in home value. This allows us to quantify how much each factor contributes to the change in median price. This objective was achieved using quantile regression modeling to estimate each contribution.

ACADEMIC PROJECTS

Forecasting Daily Exchange Rate Between British Pound and the US Dollar Using Time Series Analysis: ARIMA and GARCH models were used to predict the exchange rate and volatility of the British Pound against the US Dollar. The GARCH model was superior to its counterpart.

Predicting Apple’s Stock Prices Using Machine Learning Techniques: Multiple linear regression, support vector regression, artificial neural networks and ensemble learning were compared to the standard statistical models such as ARIMA and GARCH. This was to achieve a better predictive performance of Apple’s stock in terms of price and profitability.

Studying the Relationship Between Cancers Versus Groups Such as Gender, Race and Age in the US: Log-linear modeling was applied to establish the relationships among the various groups affected by cancer in the US.

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