Description

What you will be doing:

Analyze structured and unstructured datasets to identify trends, answer business questions, and support data-informed decision-making.
Develop, test, and refine statistical and analytical models with support from more experienced team members.
Contribute to analytics capabilities aligned with product roadmaps, customer needs, and defined business use cases.
Partner with data warehouse engineers, data engineers, product teams, subject-matter experts, and other stakeholders to develop end-to-end analytical solutions.
Prepare, clean, transform, and validate data for analysis, modeling, reporting, and experimentation.
Evaluate new data sources and analytical methods that may support product or business needs.
Document analytical approaches, communicate findings, and support the deployment and ongoing improvement of data science solutions.

What you will likely bring:

2–4 years’ experience in data science, analytics, statistical modeling, or a related field, including relevant internships, academic projects, or applied professional experience.
Working knowledge of Python and SQL for data analysis, data preparation, modeling, and visualization.
Understanding of statistical methods, model evaluation, and analytical problem-solving.
Experience working with datasets to identify patterns, test hypotheses, and communicate actionable findings.
Familiarity with database concepts, data warehousing, or data-processing workflows.
Strong written and verbal communication skills, with the ability to explain findings to technical and nontechnical stakeholders.
Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, or a related field, or equivalent practical experience.

What could set you apart:

Coursework, academic projects, certifications, or early professional experience involving artificial intelligence, machine learning, or generative AI.
Experience using Python libraries such as pandas, NumPy, scikit-learn, or similar analytical tools.
Exposure to AI or machine-learning tools, frameworks, application programming interfaces, or cloud-based AI services.
Experience applying AI or machine learning to practical business, product, or customer use cases.
Exposure to R or other data science and statistical tools.
Experience with data-visualization or business-intelligence tools such as Power BI, Tableau, or MicroStrategy.
Familiarity with cloud data platforms, distributed data-processing environments, or production analytics workflows

Key Skills

Education

Any Graduate