Amir Saleem Hello! I am

Amir Saleem

I'm a Data Scientist

Transforming raw data into actionable insights with Python, SQL & machine learning.

Islamabad, Pakistan

Resume Experience Contact Me

Skills

Tools and domains grouped by how often I use them—no invented percentages. One timeline below is anchored to real dates from my experience.

Python

8+ years in data-focused roles

From first data-science internship (2018) through present. The bar is a date span, not a skill score.

Engineering

Daily
Python Linux / Bash Git / GitHub
Occasionally
FastAPI / Django Web Scraping Bitbucket / GitLab

Data science (core)

Daily
SQL Problem Solving
Occasionally
R

Data engineering

Daily
PostgreSQL MySQL
Regularly
ETL
Occasionally
Amazon S3 Amazon Athena Amazon Redshift GraphQL

IDE & notebooks

Daily
Jupyter Cursor DBeaver
Occasionally
Colab Workbench

Data analysis

Daily
Pandas Google Sheets Pivot Tables
Regularly
Excel Data Mining Statistical Data Analysis

Machine learning

Regularly
Scikit-learn PyTorch MLOps Predictive Analytics Vision

Cloud & delivery

Regularly
CI/CD GitHub Actions
Occasionally
AWS Lambda AWS CloudWatch

Experience

Python Data Scientist

Turing
Remote (USA)

Nov 2023 - Mar 2026
(2 years, 5 months)

  • Conducted comprehensive data gathering and analysis, leveraging EDA, statistics, and machine learning techniques to derive actionable insights.
  • Formulated complex queries to drive deeper understanding across various domains, enhancing data-driven decision-making.
  • Developed and implemented robust code solutions, streamlining processes and improving efficiency.
  • Collaborated with LLM to identify performance issues, providing constructive feedback to enhance model accuracy.
  • Partnered with cross-functional teams to validate results and optimize model training, ensuring high-quality outcomes.
  • Evaluated LLM-generated code against stringent rubrics, contributing to code quality and reliability in production environments.
  • Engineered "bug-planting" scenarios, enhancing testing protocols and fostering a culture of continuous improvement.

Data Engineer ( Freelancer )

Medusa
Remote (USA)

Jun 2023 - Feb 2024
(9 months)

  • Developed and optimized ETL processes leveraging Redshift, Athena, and MySQL to enhance data accessibility and analysis.
  • Integrated CI/CD pipelines using Bitbucket to streamline deployment workflows, improving efficiency and reducing errors.
  • Automated code deployment by executing pushes that trigger Bitbucket pipelines, ensuring timely updates and consistency.
  • Oversaw Docker image deployments to AWS, utilizing CloudWatch for performance monitoring and resource optimization.
  • Executed reverse ETL processes to facilitate data extraction and transformation using GraphQL APIs, enhancing data usability for analytics.
  • Collaborated with cross-functional teams to translate complex data sets into actionable insights, driving data-informed decision-making.

Data Scientist

LoveForData
On-site (Karachi - Pakistan)

Jan 2023 - Nov 2023
(11 months)

  • ETL Development: Designed and optimized ETL processes to efficiently extract, transform, and load data from diverse sources into a robust data warehouse, enhancing data accessibility.
  • Advanced Data Analysis: Conducted in-depth data analysis using Python and SQL, uncovering actionable insights and trends that informed strategic decision-making.
  • Predictive Modeling: Engineered and deployed predictive models, leveraging machine learning techniques to forecast outcomes and drive business growth.
  • Data Visualization: Created compelling visualizations to communicate findings effectively to stakeholders, facilitating data-driven discussions.
  • Collaboration: Partnered with cross-functional teams to align data initiatives with business objectives, ensuring a comprehensive approach to data management.

Data Analyst

LoveForData
On-site (Karachi - Pakistan)

Jan 2021 - Dec 2022
( 2 years )

  • Django Development: Developed and maintained robust Django applications for enhanced data visualization and analysis, improving user engagement and decision-making.
  • Predictive Modeling: Designed and implemented predictive models that accurately forecasted future trends, leading to data-driven strategic decisions.
  • Advanced Data Analysis: Conducted in-depth data analysis using Python and SQL, uncovering critical patterns and trends that informed business strategies.
  • Market Basket Analysis: Utilized market basket analysis techniques to derive actionable insights, optimizing restaurant menu offerings and marketing strategies to enhance customer satisfaction and retention.
  • Data-Driven Solutions: Collaborated with cross-functional teams to deliver data-driven solutions that supported operational efficiency and business growth.

Junior Data Analyst

LoveForData
On-site (Karachi - Pakistan)

Apr 2019 - Dec 2020
( 1 year, 9 months )

  • Data Quality Improvement: Conducted comprehensive data audits to pinpoint and rectify inaccuracies, enhancing clients' understanding of their data integrity challenges.
  • Data Visualization: Created an interactive dashboard utilizing R-Shiny, enabling stakeholders to visualize and analyze complex datasets effectively.
  • ETL Development: Engineered robust ETL processes to extract, transform, and load data from diverse sources into a centralized data warehouse, streamlining data accessibility and reporting.
  • Analytical Skills: Leveraged statistical analysis and data modeling techniques to derive actionable insights, supporting data-driven decision-making.
  • Collaboration: Worked closely with cross-functional teams to align data strategies with business objectives, fostering a culture of data literacy.

Data Science Trainee

LoveForData
On-site (Karachi - Pakistan)

Oct 2018 - Mar 2019
( 6 months )

  • Web-Scraping: Engineered a robust web scraping application to efficiently extract and preprocess data from diverse online sources, enhancing data availability for analysis.
  • NLP: Designed and implemented an advanced NLP model for sentiment analysis, text summarization, and question answering, improving insights and decision-making capabilities.
  • Data Analysis: Conducted exploratory data analysis (EDA) to uncover trends and patterns, facilitating data-driven strategies.
  • Data Engineering: Collaborated on data pipeline development to ensure seamless data flow and integration for analytics projects.
  • Programming: Utilized Python and libraries such as Beautiful Soup, Pandas, and NLTK to optimize data extraction and analysis processes.

Data Science Intern

LoveForData
On-site (Karachi - Pakistan)

Aug 2018 - Sep 2018
( 2 months )

  • Assisted in data collection, cleaning, and organization.
  • Gained exposure to various data analysis tools and techniques.

Certifications

Fundamentals of Visualization with Tableau

Fundamentals of Visualization with Tableau

Essential Design Principles for Tableau

Essential Design Principles for Tableau

Visual Analytics with Tableau

Visual Analytics with Tableau

Introduction to Big Data with Spark and Hadoop

Introduction to Big Data with Spark and Hadoop

Python for Data Science, AI & Development

Python for Data Science, AI & Development

Python Project for Data Engineering

Python Project for Data Engineering

Databases and SQL for Data Science with Python

Databases and SQL for Data Science with Python

Hands-on Introduction to Linux Commands and Shell Scripting

Hands-on Introduction to Linux Commands and Shell Scripting

Relational Database Administration (DBA)

Relational Database Administration (DBA)

ETL and Data Pipelines with Shell, Airflow and Kafka

ETL and Data Pipelines with Shell, Airflow and Kafka

Getting Started with Data Warehousing and BI Analytics

Getting Started with Data Warehousing and BI Analytics

Introduction to NoSQL Databases

Introduction to NoSQL Databases

Introduction to Statistics

Introduction to Statistics

How Google does Machine Learning

How Google does Machine Learning

Google Cloud Big Data and Machine Learning Fundamentals

Google Cloud Big Data and Machine Learning Fundamentals

Introduction to Data Engineering

Introduction to Data Engineering

Introduction to Relational Databases (RDBMS)

Introduction to Relational Databases (RDBMS)

Google Data Analytics (Professional Certificate)

Google Data Analytics (Professional Certificate)

Foundations: Data, Data, Everywhere

Foundations: Data, Data, Everywhere

Share Data Through the Art of Visualization

Share Data Through the Art of Visualization

Ask Questions to Make Data-Driven Decisions

Ask Questions to Make Data-Driven Decisions

Prepare Data for Exploration

Prepare Data for Exploration

Data Analysis with R Programming

Data Analysis with R Programming

Google Data Analytics Capstone: Complete a Case Study

Google Data Analytics Capstone: Complete a Case Study

Process Data from Dirty to Clean

Process Data from Dirty to Clean

Python Project: pillow, tesseract, and opencv

Python Project: pillow, tesseract, and opencv

Python 3 Programming (Specialization)

Python 3 Programming (Specialization)

Introduction to Probability and Data with R

Introduction to Probability and Data with R

Using Python to Access Web Data

Using Python to Access Web Data

Using Databases with Python

Using Databases with Python

Capstone: Retrieving, Processing, and Visualizing Data with Python

Capstone: Retrieving, Processing, and Visualizing Data with Python

Python

Python

Data Collection and Processing with Python

Data Collection and Processing with Python

Python Functions, Files, and Dictionaries

Python Functions, Files, and Dictionaries

Python Classes and Inheritance

Python Classes and Inheritance

Python for Everybody (Specialization)

Python for Everybody (Specialization)

Programming for Everybody (Getting Started with Python)

Programming for Everybody (Getting Started with Python)

Python Data Structures

Python Data Structures

Linux for Developers

Linux for Developers

Linux Server Management and Security

Linux Server Management and Security

Analyzing Police Activity with pandas

Analyzing Police Activity with pandas

Introduction to Applied Machine Learning

Introduction to Applied Machine Learning

Machine Learning Algorithms: Supervised Learning Tip to Tail

Machine Learning Algorithms: Supervised Learning Tip to Tail

Data for Machine Learning

Data for Machine Learning

R Programming

R Programming

Exploratory Data Analysis

Exploratory Data Analysis

Getting and Cleaning Data

Getting and Cleaning Data

The Data Scientist’s Toolbox

The Data Scientist’s Toolbox

Excel Skills for Business: Essentials

Excel Skills for Business: Essentials

Excel Skills for Business: Intermediate I

Excel Skills for Business: Intermediate I

Introduction to Data Science in Python

Introduction to Data Science in Python

Learn to Program: The Fundamentals

Learn to Program: The Fundamentals

Skill assessments

SQL (Basic)

SQL (Basic)

SQL (Intermediate)

SQL (Intermediate)

SQL (Advanced)

SQL (Advanced)

Python (Basic)

Python (Basic)

Software Engineer

Software Engineer

Projects

End-to-End MLOps Pipeline for Credit Default Prediction

PYTHON • PANDAS • SCIKIT-LEARN • DECISION TREES • GITHUB ACTIONS • SQL

Built an end-to-end MLOps pipeline to predict customer credit defaults using a Decision Tree model, with a strong focus on automation, monitoring, and production reliability.

The system ingests data directly from a database, performs data cleaning and feature engineering, and trains a machine learning model evaluated using F1-score to balance precision and recall.

Automated retraining:
• Periodic retraining (weekly)
• Drift-triggered retraining using Population Stability Index (PSI)

Integrated CI/CD using GitHub Actions to automate model retraining, evaluation against the production model, versioned model storage (.pkl), and deployment to the API server.

Safe deployment: model versioning and rollback; blue-green deployment strategy. Monitored production performance by tracking input data, predictions, and anomalies—identifying and resolving issues such as reduced data ingestion from UI changes and performance degradation from data drift.

The system is designed for scalability, reliability, and continuous model improvement with minimal manual intervention.

Production Web Scraping at Scale

PYTHON • BEAUTIFULSOUP • WEB SCRAPING • SQLITE • CSV • PICKLE

Contributed to a 200+ scraper program covering many domains and layouts, and shipped focused extractors—for example multi-region smartphone listings from 10+ retail and classified sites, and property buy/rent feeds with wildly different markup.

Used a class-based design (scraper_class-style) to separate site-specific HTML from shared orchestration, retries, and outputs (CSV, pickle, SQLite) so analysts and pipelines could treat fresh data as infrastructure, not one-off scripts.

One Million GitHub Repos — Metadata Study

PYTHON • GITHUB SEARCH API • PANDAS • MATPLOTLIB • SEABORN

Pulled metadata for roughly one million recently created repositories via the GitHub Search API, working within pagination and rate limits to assemble a clean dataset for exploration.

Performed EDA in Pandas and built Matplotlib and Seaborn visuals to summarize languages, activity patterns, and other attributes—turning raw API responses into charts and takeaways suitable for reporting or further modeling.

Device Telemetry ETL at Scale

PYTHON • ETL • MYSQL • LINUX • AUTOMATION

Built a daily ETL pipeline for a client mobile product with a large active user base: ingest device-generated .txt payloads packaged in .zip archives, parse noisy files, join to MySQL user dimensions, and upsert facts using conditional business rules.

Processed thousands of archives per run on Linux with repeatable jobs and clear failure behavior—balancing throughput, accuracy, and fit with downstream analytics and reporting.

Monday.com to MySQL Reverse ETL

PYTHON • GRAPHQL • API • MYSQL • LINUX

Implemented reverse ETL from Monday.com into MySQL using their GraphQL API: incremental loads for a rolling 24-hour window of board and item activity, normalized for BI and ad hoc SQL.

Scheduled on Linux with cron for hands-off sync—reducing copy-paste exports and keeping warehouse-style tables aligned with the live workspace.

Multi-Source ETL & Data Quality Alerts

PYTHON • AWS REDSHIFT • ATHENA • MYSQL • DOCKER • SLACK • BITBUCKET • CLOUDWATCH

Owned ETL and data-quality checks across Redshift, Athena, and MySQL, with failures pushed to a Slack bot for rapid triage.

Wired jobs into Bitbucket CI/CD and Docker deploys on AWS, using CloudWatch for runtime visibility—so data pipelines stayed observable, repeatable, and safe to change.

Django Analytics & ML Platform

PYTHON • DJANGO • REST API • PANDAS • SEABORN

Delivered a Django application that combines EDA, trained ML models, and market basket-style analysis behind one cohesive experience and REST endpoints.

Focused on a practical split between interactive exploration, reproducible modeling workflows, and outputs stakeholders could trust—without juggling separate notebooks and ad hoc scripts.

High-Volume MySQL ETL

PYTHON • ETL • MYSQL

Engineered a Python ETL pipeline to land large, semi-structured feeds into MySQL: bespoke parsers, validation, and multi-table loads with consistent keys and transforms.

Emphasized repeatable rules, edge-case handling, and schemas that stayed query-friendly for analysts—cutting manual cleanup before reporting.

Used-Car Price Regression

PYTHON • MACHINE LEARNING • REGRESSION • FEATURE ENGINEERING

Built regression models to estimate vehicle prices from brand, model, year, mileage, and related listing attributes—end to end from prep through evaluation.

Stress-tested feature engineering and model choices for stable, interpretable signals rather than brittle leaderboard fits—oriented toward buyer/seller-facing price guidance.

Django Hiring & Assessment Portal

PYTHON • DJANGO • REST API • MYSQL • EMAIL AUTOMATION

Full Django workflow for technical hiring: secure signup, emailed credentials, and timed assessments with auto-submit when the clock runs out.

Department-aware randomized question banks, multiple difficulty tiers, and email updates to both candidates and HR on completion or expiry—cutting manual coordination while keeping attempts fair and auditable.

Data Quality Audit & Remediation

PYTHON • MYSQL • DATA QUALITY

Ran a structured audit across production MySQL datasets: duplicates, referential gaps, impossible or stale values, and silent drift that skewed dashboards.

Produced prioritized findings, partnered on fixes, and implemented corrections in code—raising trust in reporting and anything downstream (automation, ML, finance).

Telecom Customer Lifetime Value

R • MACHINE LEARNING • PREDICTIVE MODELING

Modeled CLV for telecom subscribers from tenure, usage, and revenue history—supporting segmentation, campaign targeting, and retention planning.

Balanced statistical rigor with narratives marketing and finance could operationalize, not only model metrics on a slide.

Land Valuation ML Model

PYTHON • MACHINE LEARNING • ORACLE SQL

Built a land-price model from location, parcel, and attribute signals—using Oracle SQL for curated features and Python for modeling and evaluation.

Aimed at interpretable drivers of value so real estate workflows could move from anecdotal comps to repeatable, data-backed estimates.

Interactive R Shiny Dashboard

R • SHINY • DATA VISUALIZATION

Shipped an R Shiny app for exploratory views and stakeholder-ready charts over prepared datasets—reactive inputs, clear summaries, and layout tuned for quick decisions without standing up a separate BI server.

Useful where analysts already lived in R and needed a thin, shareable UI on top of trusted transforms.

TV Show Comment Sentiment

PYTHON • NLP • MONGODB • TEXT CLASSIFICATION

End-to-end sentiment pipeline for TV discussion comments: ingest and store flexible document shapes in MongoDB, normalize text, and train/evaluate classifiers for positive vs negative tone.

Surfaced aggregate audience reaction patterns—useful for content and programming teams comparing shows or episodes at a glance.

Coding Profiles

Let's Connect

Location

Islamabad, Pakistan