DATA SCIENTIST I

Job Overview

Strong proficiency in Python for data analysis and machine learning.
Good knowledge of SQL and relational databases.
Understanding of Machine Learning algorithms, including:
Regression
Classification
Clustering

Recommendation Systems:

• Time Series Forecasting
• Experience with AI/ML libraries such as:
• Scikit-learn
• Pandas
• NumPy
• TensorFlow or PyTorch (basic knowledge)
• Understanding of statistics, probability, hypothesis testing, and feature engineering.
• Data visualization skills using Power BI, Tableau, Matplotlib, or Seaborn.
• Basic knowledge of Generative AI and Large Language Models (LLMs).
• Familiarity with cloud platforms such as AWS, Azure, or Databricks is an advantage

Responsibilities:

• Develop and validate machine learning models to address business challenges.
• Perform data exploration, cleansing, feature engineering, and model evaluation.
• Analyze large datasets to identify trends, patterns, and business opportunities.
• Support deployment and monitoring of ML models in production environments.
• Build dashboards, reports, and visualizations to communicate insights.
• Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.
• Conduct experiments and evaluate model performance using appropriate metrics.
• Document methodologies, assumptions, and results for knowledge sharing.
• Stay updated with new developments in AI, ML, and data science technologies.

Education & Experience:

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a related field.
• 0-3 years of experience in Data Science, Analytics, Machine Learning, or AI-related projects.
• Internship, academic, or industry experience in ML/AI projects is preferred.

Key Qualities:

• Strong analytical and problem-solving skills.
• Curiosity and willingness to learn new technologies.
• Good communication and presentation skills.
• Ability to work collaboratively in cross-functional teams.
• Attention to detail and quality.
• Self-motivated with a continuous improvement mindset.
• Results-oriented and customer-focused.
  

Competencies