Entry-Level Data Analyst

Job Summary:
- We are seeking a motivated Entry-Level Data Scientist to support data-driven decision-making by analyzing datasets, building basic predictive models, and generating insights.
- This role is ideal for recent graduates or candidates with 0–2 years of experience who are passionate about data, statistics, and machine learning.

Key Responsibilities:
- Collect, clean, and preprocess structured and unstructured data.
- Perform exploratory data analysis (EDA) to identify trends and patterns.
- Build and evaluate basic machine learning models.
- Develop data visualizations and dashboards.
- Write SQL queries to extract and validate data.
- Collaborate with data engineers, analysts, and business teams.
- Document methodologies, findings, and model results.
- Support deployment and monitoring of models (basic level).

Required Skills & Qualifications:
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- 0–2 years of experience (freshers welcome).
- Strong knowledge of Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn).
- Strong knowledge of statistics and probability (hypothesis testing, distributions).
- Strong knowledge of SQL (joins, aggregations).
- Basic understanding of machine learning algorithms.
- Strong analytical and problem-solving skills.
- Good communication skills.

Preferred Qualifications:
- Internship or academic project in data science or analytics.
- Experience with tools such as Jupyter Notebook.
- Experience with tools such as Power BI or Tableau.
- Exposure to deep learning (TensorFlow or PyTorch).
- Exposure to cloud platforms (AWS, Azure, GCP).
- Knowledge of data pipelines or big data tools (Spark – basic).

Key Deliverables:
- Cleaned datasets and data pipelines (basic level).
- Data analysis reports and dashboards.
- Machine learning models (baseline models).
- Documentation of insights and recommendations.

Location: Remote - United States

Skills required for this job:

• Cloud platforms
• Dashboards
• Data analysis
• Data science
• Deployment
• Event-driven architecture (EDA)
• Exploratory data analysis
• Machine learning
• Monitoring
• NumPy
• Pandas
• Python
• SQL
• Statistics
• Tableau