ML Engineer, Software Engineer

OLGA VASILEVA

+1-857-***-**** ************@*******.*** Jersey City, NJ

ï olgavasileva § olgavasileva fprojects

ENCLOSURES

• cover letter

• resume

• 2 letters of

recommendation

COVER LETTER

I’m writing to express my interest in your open role. With a strong background in software development and a deep passion for mathematics and modeling, I bring a unique blend of engineering rigor and scientific thinking to solving complex, real-world problems with data. I hold a Master’s degree in Data Science and Bachelor’s degrees in Computer Science and Economics. Before transitioning into data science, I spent over seven years as a full-stack developer, making key architectural and implementation decisions in late-stage startup environments where I naturally wore multiple hats. Most recently, I’ve been building credit risk models at a fintech company, developing ML pipelines from scratch and managing experiments using tools like MLflow and Azure ML. What motivates me most is combining my lifelong interest in mathematics with real engineering impact—building systems that are robust, scalable, interpretable, and useful. I thrive in environments where I can take ownership of projects, be held accountable for their success, and contribute meaningfully to the overall impact. I’m drawn to opportunities where I can leverage my engineering expertise while continuing to grow as a data scientist in a collaborative, intellectually curious environment.

Thank you for considering my application. I’d welcome the opportunity to further discuss how my skills and background align with your team’s goals. Warm regards,

Olga Vasileva

OLGA VASILEVA

+1-857-***-**** ************@*******.*** Jersey City, NJ ï olgavasileva § olgavasileva fprojects

EXPERIENCE

• Vox Funding Machine Learning Engineer / Data Scientist (New York, NY) December 2024 - current

Owning the development of the ML-based credit risk model for the SMB segment.

Building and maintaining proprietary ML Framework for the end-to-end ML lifecycle: data preprocessing, training, evaluation, and experiment tracking.

Running experiments for model performance comparison using MLflow.

Utilizing Azure for computing, ML pipeline orchestration, and model deployment.

Technologies: Python, SQL Server, Azure ML, XGBoost, Regressions, scikit-learn, MLflow, and others

• Academic Leave June 2022 - December 2024

• The Black List Software Developer / Full Stack Engineer (Boston, MA/Remote) June 2016 - June 2022

Lead developer, reporting to the CTO and owning a significant part of the application; responsible for the end-to-end design, implementation, and technical decision-making for both the user-facing web application and admin-side tools.

Independently designed a significant portion of the database architecture, developed backend modules, and build dynamic UI/UX interfaces.

Created user/admin dashboards and premium analytic tools integrating data from multiple sources.

Developed a direct messaging system and automated reporting tasks, including pdf report generation, email delivery, calculating and populating summary database tables with updated metrics and stats.

Led comprehensive codebase refactoring and total web app redesign alongside the CTO.

Integrated third-party APIs and built custom libraries from scratch.

Technologies: Perl, JavaScript, React, Linux, MySQL, AWS (EC2, S3), CSS, HTML, Foundation.

• Statisfy (Early Stage Startup) Software Developer / Full Stack Engineer (Boston, MA) June 2015 - June 2016

Worked on the test-driven of a Ruby on Rails application designed to host interactive article-based content with embedded polls, aimed at collecting voter data for marketing purposes.

Collaborated with CEO/CTO on enterprise product enhancements.

Participated in the design and development of consumer-facing and enterprise-level products using the MVC design pattern and Agile/Scrum methodologies.

Developed the company’s main website.

Built the enterprise dashboard that allowed clients to create, deploy, and analyze interactive content.

Contributed to the development and enhancement of the internal Admin Dashboard.

Technologies: Ruby on Rails, CoffeeScript, Linux, MySQL, CSS, Haml, JavaScript, JQuery, Bootstrap, Redis. EDUCATION

• Illinois Institute of Technology MS Data Science, GPA: 3.96/4.00 (USA) August 2023 - December 2024

Coursework: Regression, Statistical Learning, Time Series, Data Preparation and Analysis, Bayesian Statistics, Deep Learning, Relational Databases, Big Data, Scientific Writing, R and Python Programming.

• Self-teaching Mathematics and Statistics June 2022 - August 2023

Undertook a year of intensive self-study to refresh and expand existing knowledge.

Areas: Calculus, Statistics, Linear Algebra, Deep Learning.

• University of Massachusetts Boston BS Computer Science (USA) August 2012 - May 2016

Coursework: C++, C, Java, Assembler, Code Architecture, Systems Design, etc.

• South Ural State University BS Economics and Management (Russia) September 2008 - May 2012 SKILLS

• Statistical Learning:Calculus, Linear Algebra, PCA, LDA, SVM, Algorithms (KNN, K-Means, Decision Trees, etc.), Regression, Regularization, MLE, and more.

• Machine Learning:Data Engineering and Imputing, CNN, RNN, Maxout, RMSProp, LSTM, GAN, Self-Attention, DANN, pruning, distillation, regularization, dropout

• Programming Languages:Python, R, Perl, Ruby, C++, SQL, Bash scripting.

• Libraries:TensorFlow, Scikit-learn, PyTorch, NumPy, Pandas, Pyplot, Seaborn and others.

• Web Technologies: JavaScript, React, HTML, CSS, Foundation.

• Database Technologies:MySQL, Cassandra, HBase, Azure Data Lake, Hadoop.

• Cloud Technologies:AWS: EC2, EMR, S3.

• Other Tools & Version Control:Linux Shell, Git, APIs, Regex, Jupyter, Google Colab, Vim, VS and more CERTIFICATIONS

DeepLearning.AI : Deep Learning Specialization, IBM : Applied Data Science, Google : Google Business Intelligence

May 8, 2025

To whom it may concern,

It is my pleasure to recommend Olga Vasileva for a position in your company. Olga is among the most driven students I have had the pleasure of working with in my seven years of teaching, at Illinois Tech and other schools (University of Wisconsin, University of Southern California). Of the students that I've taught in Illinois Tech's Master of Data Science program, I'd rank Olga in the top 10{15% in terms of ability and potential (solidly top 10% among the online cohort). I believe she'd make a great addition to your team. Olga was a student in the Time Series Analysis course (Math 546) that I taught during the summer of 2024. This course covers the basics of univariate time series modeling, with a strong emphasis on ARIMA models (both their mathematical and statistical underpinnings and their actual implementation in R). The required components of the course were run online and asynchronously, but Olga was one of a handful of students that took every opportunity to interact with me and her peers during the optional live sessions and o ce hours. As a result, I got to know Olga better than I know many of the students in the in-person version of the course that I'm currently teaching. Olga was one of the two most active participants in these sessions and consistently asked insightful questions about the course material that not only improved her understanding but also helped me to re ne my own teaching strategies. She and I also exchanged a number of emails during the semester. Many of these were additional questions about the course material, and quite a few others concerned corrections to the course resources that I'd developed and posted. The lesson readings and assessment questions are now a good deal cleaner than they were last summer, in no small part due to Olga's contributions.

I would also like to comment on Olga's mathematical abilities. I nd that the students who enroll in Math 546 by and large are pretty adept at the practical tasks of writing and working with R code. But most struggle with the more theoretical mathematical and statistical aspects of the course. Olga was initially no exception to this struggle. But unlike most of her peers, she persisted through these di culties, leveraged the resources that were available to her, and asked lots of questions. It paid o my impression is that by the end of the semester, Olga's understanding of the mathematics was better than the vast majority of her peers'. The bottom line: Olga is capable of understanding complex topics, and she is able and willing to put in the work to get there.

For the reasons above, I hope you will give Olga your full consideration. Please do not hesitate to contact me if you have further questions regarding Olga's application. Sincerely,

Trevor Leslie

*******@***.***

Assistant Professor of Applied Mathematics

Illinois Institute of Technology

ILLINOIS INSTITUTE OF TECHNOLOGY

Department of Applied Mathematics

Sara Jamshidi

Adjunct Professor

May 7, 2025

To Whom It May Concern,

I am pleased to write this letter of recommendation for Olga Vasileva, who was a student in my classes Math 569: Statistical Learning and Math 574: Bayesian Computational Statistics at Illinois Tech. Olga demonstrated exceptional dedication and intellectual curiosity throughout both courses, earning an A in each.

Olga stands out as one of the few students who pushed herself to thoroughly learn the material by selecting highly complex projects. For instance, in her final project for Math 569, she analyzed stu- dent achievement in secondary education using a dataset from two Portuguese schools. The dataset included student grades, demographic information, social factors, and school-related features. Olga divided the dataset into two parts, focusing on performance in Mathematics and Portuguese lan- guage. She modeled the data under binary/five-level classification and regression tasks, replicating work from (Cortez and Silva, 2008). Notably, she addressed the challenge of predicting the final year grade (G3), which is highly correlated with the first and second period grades (G1 and G2), demonstrating her ability to work with intricate data relationships and derive meaningful insights. Only two other students out of about 250 provided this level of work. Olga’s ability to grasp complex statistical concepts and apply them effectively to the real world is also evident. In Math 574, for example, she analyzed animal adoption patterns using data from the Austin Animal Center. She developed a Bayesian hierarchical model to identify adoption rate factors, using Animal Center Intakes and Outcomes datasets. Olga handled complex data prepro- cessing tasks, including merging, responsibly handling missing values, and variable encoding. She demonstrated a deep understanding of Bayesian methods, implementing and accurately interpreting a model to determine adoption likelihood.

In both classes, she consistently exhibited a strong work ethic, responsibility, and a keen intellect, which are qualities that any employer or academic institution would value highly. Given this as well as Olga’s background in data science and software development, she is the ideal candidate. 10 West 32nd Street

Chicago, IL 60616 USA

Tel: 312-***-****

Fax: 312-***-****

Email: *********@***.***

I have no reservations in recommending Olga for any position or academic pursuit she may seek. I am confident that she will be a valuable asset to any team or organization fortunate enough to have her. If you require any further information, please do not hesitate to contact me. Sincerely,

Sara Jamshidi

——

P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th Future Business Technology Conference (FUBUTEC 2008), pages 5–12, Porto, Portugal, April 2008. EUROSIS, ISBN 978-***-****-39-7. 2

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