Help & Supportcareers@gravityer.com
Not Actively on LinkedIn.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I am passionate about Science, Technology, Business and the intersection between these fields as key drivers for social and economic growth.
Carnegie Mellon University
Doctor of Philosophy - PhD, Computer Science
August 1, 2019 – June 1, 2024
Stanford University
Master’s Degree, Computer Science
January 1, 2016 – January 1, 2017
Stanford University
Bachelor’s Degree, Physics
January 1, 2013 – January 1, 2017
Software Engineering Intern
June 1, 2016 – September 1, 2016
SLAC National Accelerator Laboratory
Research Assistant in High Energy Physics + Machine Learning
March 1, 2016 – March 1, 2018
Stanford
Engineering Practicum Intern, Google Analytics
June 1, 2015 – September 1, 2015
Mountainview, California
Stanford University
Section Leader, CS106 Series
April 1, 2015 – June 1, 2018
Stanford University
Human Resource Assistant
November 1, 2014 – May 1, 2015
Verbatm
Software Developer
August 1, 2014 – May 1, 2015
Stanford
Ahonya.com
Sofware Intern
June 1, 2014 – August 1, 2014
Accra, Ghana
Predicting Peak Exchange Rate Times
September 1, 2015 – December 1, 2015
Produced model for predicting day of peak US Dollar to Kenyan Shilling Exchange Rate in a given window of time Used model selection on Locally Weighted Linear Regression, Softmax and SVM regressions amongst others to derive optimized model that combined selected Macro-Economic features like Inflation with Exchange rate time series data to obtain twice the accuracy of a random process model.
Callback Newsfeed
December 1, 2014 – Present
Dynamically updating newsfeed for multimedia that interfaces Fickr, Youtube and Soundcloud APIs. Implemented server using Node.js and Express.js relying on MongoDB for managing structured data
Heap Allocator
June 1, 2014 – Present
Designed and implemented Dynamic Memory Management Scheme in C: malloc, realloc and free functions. Used a segregated fit coupled with doubly linked free-lists to achieve 100% throughput and 80% utilization relative to libc standard.
Cultural Fit Analysis
The candidate's academic background from top-tier universities and internships at Google indicate a drive for excellence and a strong work ethic. The variety of projects, from low-level systems to machine learning and web development, suggests intellectual curiosity and a willingness to tackle diverse challenges. The role as a Section Leader also points to a collaborative and supportive nature. However, the target role of 'Data Analyst' might not fully leverage the candidate's deep research and software engineering background, potentially leading to a mismatch in long-term career aspirations if the role is purely analytical without significant ML/engineering components.
Soft Skills & Operational Fit
The candidate's experience as a Section Leader at Stanford University suggests strong communication and mentorship skills. Project descriptions indicate problem-solving abilities and a structured approach to technical challenges. The diverse project portfolio implies adaptability and a proactive learning attitude. However, without psychometric test results, a comprehensive assessment of work attitude, stress handling, and team collaboration is not possible.