Amna
Mnazar

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Finished gigs

Finished jobs

Website

app.lunor.ai

Country

Not specified

Skills

Web, Mobile & Software Dev

Data Entry

MySQL

Python

SQL

Data Science & Analytics

Specialization

Data Scientist & Machine Learning Engineer | Researcher

Hourly rate

$70/hr

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About me

Data Scientist with an MSc in Artificial Intelligence & Robotics, specializing in transforming complex datasets into actionable insights. My adaptability is demonstrated by top rankings on competitive platforms like Lunor, where I have excelled in diverse challenges—from financial forecasting (e.g., 2nd Place in FOMC Rate Prediction) to environmental science (e.g., 3rd Place in Coral Reef Analysis). I am highly proficient in Machine Learning, Time Series Analysis, NLP, and Python. My core strength, however, is my ability to rapidly master new domains and technologies. This is further evidenced by my exploratory work in other programming fields, such as game development with GDevelop and smart contract concepts with Solidity. I am not confined to a single industry but am driven by a curiosity to extract insights from data anywhere—be it finance, crypto, environmental science, or business intelligence. Let’s collaborate to turn your data into success!

Work experience

Lunor Quest (Competitive Data Science Platform)

March 2024 – Current time

Job title

Top-Ranked Data Scientist & Machine Learning Engineer

Work experience & achievements

Competed and achieved top rankings in international data science challenges, demonstrating expertise across diverse domains including finance, ecology, public policy, and real estate. Secured 2nd Place in the "Predict FOMC Interest Rate Decision" challenge by developing a multiclass classification model using economic indicators and NLP on FOMC statements. Secured 3rd Place in the "Conch Republic Coral Quest" by performing advanced time-series and spatial analysis on coral reef data to forecast ecological trends for conservation. Secured 3rd Place in the "French Fiscal AI Innovation Challenge," analyzing 40 years of municipal tax data using DuckDB and SQL, despite no prior French language skills. Developed, trained, and validated machine learning models for prediction and forecasting; created comprehensive, client-ready reports translating complex technical findings into actionable business insights for non-technical stakeholders.

Digi Next Artificial Intelligence Camp (Startup)

October 2022 – February 2024

Job title

Co-founder & ML Engineer

Work experience & achievements

Co-founded a startup within a competitive AI incubator, taking a project from concept to development. Engineered a sentiment analysis system using BERT and FastText to extract insights from cryptocurrency-related tweets and news. Explored full-stack development by creating a game prototype, utilizing Gdevelop game engine, JavaScript, and Django/PostgreSQL for backend services.

Freelance

January 2022 – August 2022

Job title

Data Scientist

Work experience & achievements

Delivered an industry-specific solution for livestock management by building a time-series model to predict animal weight and food requirements, involving significant data cleaning and preprocessing. Executed a computer vision project for agriculture, using image processing in Python and Matlab to identify defective products and monitor growth conditions.

Education

Shiraz University

Graduation year: 2021

Level of study

PGCE

Major / Field of study

Artificial Intelligence And Robotics

Kashan University

Graduation year: 2016

Level of study

Bachelor

Major / Field of study

Computer Science

No gigs added yet

Advanced Predictive Modeling for FOMC Interest Rate Decisions

Developed a high-performance, multi-class classification ensemble to forecast the Federal Reserve's interest rate decisions. The solution leveraged advanced feature engineering from historical economic indicators (FRED) and incorporated time-series decomposition to capture cyclical trends. The core methodology involved a sophisticated ensemble of Random Forest, XGBoost, and LightGBM models, refined through rigorous hyperparameter tuning. To ensure transparency and insight, the model utilized SHAP analysis for explainability, identifying the key drivers behind each prediction. This approach, praised by judges for effectively blending domain expertise with data science, secured a 2nd Place finish on the Lunor platform by delivering robust and interpretable forecasts. Core Techniques: Ensemble Modeling (Random Forest, XGBoost, LightGBM), Hyperparameter Tuning, Feature Engineering, SHAP Explainability, Time-Series Decomposition.

Florida Keys Coral Reef Ecosystem Forecast

Conducted an in-depth ecological data analysis to predict long-term trends in coral health within the Florida Keys National Marine Sanctuary. Leveraged data from the Coral Reef Evaluation and Monitoring Project (CREMP) and pollution datasets. Utilized exploratory data analysis and statistical modeling to assess coral cover, species richness, and the impact of environmental factors, providing actionable insights for conservation strategies. Achievement: Secured 3rd Place on the Lunor platform leaderboard. Techniques: XGBoost, Mann-Kendall trend analysis, PCA, simplified narratives for public audiences Tags: Exploratory Data Analysis, Research, Statistics, Python, Environmental Science, Data Visualization

Coral.pdf

French Municipal Tax Revenue Analysis and Predictor

Analyzed 40 years of French municipal tax data in collaboration with the EU Commission's Joint Research Centre (JRC). Performed data mining on a large-scale dataset using DuckDB and SQL to identify trends, correlate population size with revenue, and assess the fiscal impact of policy changes. Built predictive models to classify municipal revenue growth, showcasing the ability to derive insights from complex, domain-specific data. Achievement: Secured 3rd Place on the Lunor platform leaderboard. Tags: Machine Learning, Exploratory Data Analysis, SQL, DuckDB, Research, Public Policy

Venture Capital Investment Success Predictor

Explored the startup and venture capital investment landscape through comprehensive data analysis and machine learning. Processed and engineered features from datasets encompassing startup characteristics, funding rounds, and investor profiles. Developed predictive models to identify factors influencing startup success and investment outcomes, demonstrating the ability to uncover patterns in complex business ecosystems. Achievement: Achieved a Top Rank on the Lunor platform leaderboard. Tags: Data Analysis, Machine Learning, Data Processing, Python, Venture Capital, Predictive Modeling

vc.pdf

Dubai Real Estate Market Analysis and Price Prediction Engine

Led a comprehensive analysis of Dubai's real estate market, performing EDA on over 700,000 sales transactions to uncover key trends in pricing, volume, and prime locations. Developed a high-performance machine learning pipeline using LightGBM, engineering critical features like historical price-per-square-meter trends. The model achieved a 0.91 R² score, accurately predicting property sale prices. Furthermore, I quantified the impact of macroeconomic factors, identifying GDP growth and housing inflation as primary market drivers. This project delivered both a robust predictive model and actionable business intelligence for strategic investment decisions. Core Techniques: Machine Learning (LightGBM, CatBoost), Feature Engineering, EDA, Macroeconomic Analysis Tags: Machine Learning, Predictive Modeling, Real Estate, Python, Data Analysis

DubaiRealEstate.pdf

AmnaMnazar

Work experience

Education

Advanced Predictive Modeling for FOMC Interest Rate Decisions

Florida Keys Coral Reef Ecosystem Forecast

French Municipal Tax Revenue Analysis and Predictor

Venture Capital Investment Success Predictor

Dubai Real Estate Market Analysis and Price Prediction Engine

Amna
Mnazar