Data Scientist I

Job description

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

Values: Integrity, Accountability, Inclusion, Innovation, Teamwork