Experience – 5 to 9 yrs
4–5 years of hands-on experience in data science projects, preferably across different domains.
Robust statistical expertise, including hypothesis testing, linear and non-linear regression, classification techniques, and probability distributions.
Proven ability to translate complex business problems into data science use cases with practical impact.
Experienced in building and validating machine learning models, including classification, regression, and survival analysis.
Proficiency in Python, including libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, as well as SQL for data querying and analysis.
Experience handling both structured and unstructured datasets, with expertise in exploratory data analysis (EDA) and data cleaning.
Solid communication skills, with the ability to explain technical concepts clearly to non-technical stakeholders.
Familiarity with version control tools such as Git/GitHub and team-oriented development workflows.
A deployment mindset, with an understanding of how to build data products that are usable, scalable, and maintainable.