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Machine Learning Engineer | Data Scientist
๐๐๐ง๐ ๐ฎ๐๐ ๐๐ฌ: Python, SQL ๐๐๐๐ฉ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ง๐ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ : PyTorch, TensorFlow, XGBoost, Scikit-Learn ๐๐๐ญ๐ ๐๐๐ง๐ข๐ฉ๐ฎ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Pandas, NumPy, Matplotlib, Seaborn, Plotly, Grafana, SHAP, LIME ๐๐ข๐ฌ๐ญ๐ซ๐ข๐๐ฎ๐ญ๐๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ : Dask, Rapids (cuDF, cuPY, cuML), BlazingSQL, Horovod ๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐๐ก๐๐ฌ๐ญ๐ซ๐๐ญ๐ข๐จ๐ง: Valohai (MLOps), Flask, FastAPI, Docker, Kubernetes, Git, GitHub ๐๐ฅ๐จ๐ฎ๐ ๐๐ง๐ ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ: AWS (SageMaker, EC2, S3), Codespaces, Cloud9, MySQL, PostgreSQL, SQLite, Snowflake ๐๐๐ง๐ ๐ฎ๐๐ ๐ ๐๐จ๐๐๐ฅ๐ฌ ๐๐ง๐ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐ ๐๐: GPT-4 API, ChatGPT, Grok, LangChain, DALL-E3, MidJourney, Stable Diffusion ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ ๐๐ง๐ ๐๐จ๐๐ข๐ง๐ ๐๐ฌ๐ฌ๐ข๐ฌ๐ญ๐๐ง๐ญ๐ฌ: AutoGPT, BabyAGI, SuperAGI, AutoGen, GPT Engineer, MetaGPT, ChatDev, Quivr ๐๐๐ ๐ ๐ฅ๐ ๐๐ฑ๐ฉ๐๐ซ๐ญ ๐๐๐ฏ๐๐ฅ ๐๐๐ง๐ค๐ข๐ง๐ : Top 3% in the World (3689 of 118034) ๐๐จ๐จ๐ ๐ฅ๐ ๐ ๐จ๐จ๐๐๐ซ (๐๐ง๐ฏ๐ข๐ญ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ฅ๐ฒ): Passed Level 4.1 - Minions Bored Game
The University of Texas at Dallas
MBA, Master of Business Administration
January 1, 2007 โ January 1, 2009
University of Arizona
BS, Chemical Engineering
January 1, 2000 โ January 1, 2004
Stealth Startup
Senior Machine Learning Engineer
January 1, 2022 โ Present
Capital One
Senior Machine Learning Engineer
January 1, 2021 โ January 1, 2021
Stealth Mode Startup Company
Senior Data Scientist
January 1, 2019 โ January 1, 2021
Firefly XD
Machine Learning Engineer
January 1, 2018 โ January 1, 2019
Tealium
Data Scientist
January 1, 2017 โ January 1, 2018
Pedrina Capital
Quant Trader
January 1, 2009 โ January 1, 2017
Texas Instruments
Senior Chemical Engineer
January 1, 2006 โ January 1, 2009
Freeport-McMoRan
Chemical Engineer
January 1, 2005 โ January 1, 2006
Orbital ATK
Rocket Engineer
January 1, 2003 โ January 1, 2003
Data Engineer
Udacity
June 24, 2026 โ Present
Machine Learning Engineer
Udacity
June 24, 2026 โ Present
Data Scientist
Udacity
June 24, 2026 โ Present
Cultural Fit Analysis
The candidate has worked in various environments, from stealth startups to large corporations like Capital One and Texas Instruments, indicating adaptability to different organizational cultures. The diverse project experience, including recommendation systems, AutoML platforms, and quant trading, suggests a broad interest in applying ML across different domains. The career trajectory shows a strong drive for continuous learning and skill acquisition, which is generally positive for cultural fit in dynamic tech environments. However, the lack of specific project details beyond bullet points makes it difficult to assess collaboration style or initiative in team settings.
Soft Skills & Operational Fit
The candidate's resume highlights leadership in integrating ML solutions and designing monitoring dashboards, suggesting strong problem-solving and operational awareness. The transition from Chemical Engineering to Quant Trading and then to ML Engineering demonstrates adaptability and a continuous learning mindset. However, without psychometric test results, a deeper assessment of work attitude, stress handling, and team collaboration is not possible.