Data Scientist

Role Overview

Data Scientist will work with structured and unstructured datasets to discover meaningful patterns, develop predictive models, and generate data-driven insights that support business objectives and strategic decision-making.

What You Will Do

Collect, clean, process, and analyze large datasets from multiple sources to identify patterns, trends, and meaningful insights. Perform exploratory data analysis (EDA) to understand data distributions, relationships, and potential business opportunities.

Why It Might Be a Fit

Ideal for an early-career professional who is passionate about statistics, programming, machine learning, and solving real-world problems through data.

Requirements

• Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related technical field.
• 0–1 year of experience in Data Science, Machine Learning, Data Analytics, or a related technical role.
• Strong programming knowledge in Python and familiarity with data science libraries such as Pandas, NumPy, and Scikit-learn.
• Solid understanding of statistics, probability, linear algebra, and mathematical fundamentals.
• Familiarity with machine learning concepts, including supervised and unsupervised learning.
• Understanding of data preprocessing, data cleaning, feature engineering, and exploratory data analysis.
• Proficiency in SQL for querying, joining, filtering, and analyzing datasets.
• Familiarity with data visualization tools and libraries such as Matplotlib, Seaborn, or Plotly.
• Understanding of machine learning algorithms such as regression, decision trees, random forests, clustering, and classification.
• Familiarity with Jupyter Notebook, Google Colab, or similar development environments.
• Basic understanding of model evaluation, validation techniques, and performance metrics.
• Familiarity with Git and GitHub for version control and collaborative development.
• Strong analytical thinking, critical reasoning, and problem-solving abilities.
• Ability to interpret complex data and communicate findings clearly.
• Strong attention to detail, teamwork, and willingness to learn new technologies.

Benefits

• Opportunity to contribute to an innovative, AI-powered technology company.
• Hands-on exposure to real-world data science and machine learning projects.
• Opportunities to strengthen programming, statistical analysis, and machine learning skills.
• Collaborative environment that encourages experimentation, innovation, and continuous learning.
• Opportunities for professional development and technical career growth.
• Exposure to modern data science tools, technologies, and analytical workflows.
• Opportunity to contribute to data-driven solutions and meaningful business initiatives.