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Popular early-mid data role, metro location, and broad generic skillset increase candidate competition.
Core data science skills (Python, SQL, EDA) transfer easily across industries, so sensitivity is low.
Explicit 2–4 years requirement plus mandatory Python, SQL, and visualization skills increases filter strictness.
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Support analytics initiatives through data cleaning, validation, exploratory data analysis, and basic model development.
Develop and validate basic statistical and machine learning models under supervision, including feature engineering and model testing.
Create reports, dashboards, and visualizations using tools like Power BI, Tableau, or Excel; document analysis steps and communicate findings to stakeholders.
Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or related field.
2-4 years of experience in Data Science workflow.
Proficiency in Python or R (including pandas, NumPy, scikit-learn), SQL, and visualization tools such as Power BI, Tableau, or Excel.
Basic understanding of statistics and machine learning concepts.
Early-career data science professional developing foundational skills in data preparation, modeling, and analytics.
Ability to work collaboratively with business, IT, and senior analytics teams to translate data into actionable insights.
Focus on accuracy, adherence to coding and data standards, and continuous learning to improve analysis quality and efficiency.