Jeremy Grace
ML EngineerData Scientist at Microsoft
Microsoft
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Scientist at Microsoft
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Inquisitive, applied research professional. Scientist with a reputation for systematically addressing complex data challenges - in the areas of feature development, experimentation, and applied machine learning. Articulate communicator with client-facing and cross-functional team experience. Concentrations: • Frequentist/Bayesian Statistics • Machine Learning • Natural Language Processing • Experimentation
UC San Diego
Big Data Specialization, Computer Science
January 1, 2016 – January 1, 2016
University of New Haven
Master of Science (M.S.), Data Science
January 1, 2016 – January 1, 2017
Murray State University
Bachelor of Science (B.S.), Sociology
January 1, 2004 – January 1, 2009
Microsoft
Data & Applied Scientist II, Microsoft Teams
September 1, 2020 – Present
Microsoft
Data & Applied Scientist, Microsoft Gaming
June 1, 2018 – September 1, 2020
Silicon Valley Bank
Data Scientist - Machine Learning
March 1, 2017 – June 1, 2018
San Francisco, California
Indatus
Data Analyst, Network Operations Manager
November 1, 2014 – October 1, 2015
Louisville, Kentucky Area
Prima LLC
Marketing Analyst, Senior Account Manager
February 1, 2011 – August 1, 2014
Louisville, Kentucky Area
Modeling Trends in Credit Card Data: Data mining and k-modes clustering algorithm
March 1, 2017 – August 1, 2017
Machine learning • Scikit-learn • k-modes • NumPy • Pandas • Bokeh • SQL • NLP • Clustering
Cultural Fit Analysis
The candidate has a strong background in data science and applied science roles, primarily within large tech companies (Microsoft) and financial institutions (Silicon Valley Bank). This suggests experience in structured, goal-oriented environments. The personal project demonstrates initiative and self-directed learning. However, without more detailed project descriptions or involvement in diverse team structures, it's challenging to fully assess cultural fit beyond a general alignment with data-driven organizations. The transition from a Sociology background to Data Science indicates a strong drive for career change and continuous learning.
Soft Skills & Operational Fit
The candidate's resume indicates a progression through various data-related roles, suggesting adaptability and a capacity for growth. The lack of specific project details or team collaboration descriptions makes it difficult to assess soft skills like teamwork, leadership, or problem-solving approach. The psychometric test score is not provided, so no assessment can be made on logical reasoning, work attitude, stress handling, or team collaboration.