Sanja Simonovikj

Data Scientist @ Indeed | MIT MEng 2021
Deep Learning, Machine Learning, AI


About Me

I am Sanja (Sanya) Simonovikj, and as of July 2021 I am a Junior Data Scientist at Indeed in Austin, TX. I am part of the Job Seeker Taxonomy and Metadata Team, which provides structured metadata used all across Indeed.

In June 2021 I graduated with a Master of Engineering (MEng) degree in Computer Science and Engineering with a concentration in AI at MIT with the class of 2021. I graduated with a Bachelor's of Science in Computer Science and Engineering at MIT with the class of 2020.

I have experience delivering valuable technical solutions using the latest AI/ML technologies to address the business needs of both large companies and start-ups. I am eager to continue using my diverse skillset to make meaningful and positive change in the world.

Industry Experience

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Junior Data Scientist - - present - Austin, TX

Job Seeker Metadata and Taxonomy Team

  • Build, experiment and analyze production level machine learning models for occupation categorization in both domestic and international markets
  • Research, conceptualize, implement and evalute custom techniques and tools to improve the team's development process and foster transparency
  • Lead an internal Deep Learning workshop, where tasks include: logistics, research materials and develop a syllabus, leading or facilitating sessions, continously improve the workshop based on feedback etc
python tensorflow extended kubeflow pandas aws git jira
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Data Science intern -

Development of intelligent component for the new Fluid collaborative technology in the Office 365 suite

  • Created a prototype of an intelligent component in Office predicting when text can be better represented as a table
  • Engineered features and optimized lightweight models that perform at least 20% better than a heuristic baseline
python optuna pandas scikit-learn microsoft azure git
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IBM Research

Research Intern - - Cambridge, MA

Machine Commonsense Reasoning - visual intuitive physics

  • Increased accuracy of perception and inference modules detecting implausible scenes in videos by over 15%
  • Developed evaluation, debugging and viz pipeline that helped detect and resolve critical framework deficiencies
python pytorch pybullet docker git/github streamlit
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Engineering Intern - - - San Diego, California

Internal tools - automatic triage of build and test failures for the Secure Systems Group

  • Performed automatic triage of build and test failures by detecting failure signatures using ML and NLP techniques
  • Built and deployed an end-to-end framework as a team internal tool, reducing debugging time from hours to seconds
python scikit-learn pytorch git/perforce html
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Data Science Intern - - - Bangalore, India

Internal tools - Text summarization tool for strategy teams

  • Developed novel Text Summarization pipeline combining extractive algorithms and abstractive Deep Learning models
  • Created an internal news processing tool for strategy teams, allowing for faster and more informed decision making
python nltk tensorflow elasticsearch selenium scrapy git/github
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Base Operations

Software Engineering Intern - - Cambridge, Massachusetts

Developing app for street-level threat assesment

  • Served as a backend developer and data engineer in an early-stage start-up
  • Implemented from scratch and deployed in production the backend of major features as well as the ETL for tweets and analysis
python flask SQL jQuery postman git/github
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Black Labs

Software Engineering Intern - - - Chihuahua, Mexico

Smart Digital Government - development of an assistant chatbot

  • Developed and improved an assistant chatbot by using ML and NLP techniques
  • Added voice control and audio output, ability to answer unseen questions, increased coherence and quality of answers
python javascript html/css nltk scrapy git/github

Research and Teaching Experience

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CSAIL - Improbable AI Lab

Graduate Research Assistant - - May 2021 - Cambridge, MA

Priors in Deep Learning (Computer Vision) - supervised by Prof. Pulkit Agrawal

  • Research is theoretical Deep Learning to induce useful priors to learn more robust and human-aligned features
  • Designed, executed, analyzed and presented experiments,
  • Produced a final and a milestone report with novel experimental results which are guiding further research
python pytorch bash scripting jupyter notebooks streamlit git/github
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CSAIL - ALFA Lab (Anyscale Learning For All)

Undergraduate researcher - -

Code representation and malware classification - supervised by Prof. Una-May O'Reilly

  • Developed Deep Learning models (Tree-Structured VAEs, Seq2Seq) to detect malicious PowerShell scripts
  • Fully curated large unlabeled and unstructured open-source PowerShell dataset
  • Co-authored a resulting research paper as a first author: Representation Learning for Code Malware
  • Poster presenter at IBM AI Research Week, AI Horizons Colloquium (Sept. 2019)
python pytorch powershell bash streamlit git/github
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MIT Electrical Engineering and Computer Science Department

Teaching Assistant - - May 2021 - Cambridge, MA (remote)

Teaching Assistant (TA) for a popular Introduction to Machine Learning course (6.036)

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MIT Electrical Engineering and Computer Science Department

Lab Assistant - - December 2018 - Cambridge, MA

Lab Assistant (LA) for a popular Introduction to Machine Learning course (6.036)



HackMIT 2019 - overall winner


AI@MIT Labs - applied AI at MIT

Community Watch

Web app

Multi-Task Learning

Final project for Meta-Learning

Semi-Sup. VAEs

Final project for Machine Learning

Models of code

Final project for NLP


Massachusetts Institute of Technology

Master of Engineering in Computer Science and Engineering


GPA 5.0/5.0
  • AI Concetration
  • Research / Teaching Assistant
  • Selected coursework: Meta Learning (6.883), Software Studio (6.172)

Massachusetts Institute of Technology

Bachelor's of Science in Computer Science and Engineering


GPA 4.7/5.0
  • Undergraduate Researcher, Lab Assistant, Grader
  • Selected Coursework:
    • Intro/Advanced Machine Learning (6.036/6.867)
    • Advances in Computer Vision (6.819)
    • Advanced Natural Language Processing (6.864)
    • Design and Analysis of Algorithms (6.046)
    • Introduction to Inference (6.008)
    • Elements of Software Construction (6.031)