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Tier-1 brand, mid-level ML role, and popular Data Scientist title increase candidate competition.
Core ML, NLP, and data engineering skills are transferable across industries, though support-domain experience adds moderate bias.
Explicit 3–5 years requirement plus mandatory ML/NLP, Python, SQL, and BI tooling increases shortlisting rigidity.
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Develop and implement data analysis methods and machine learning models to generate actionable insights and solve business problems in CEE operations.
Build and maintain automated data pipelines and collaborate with data engineers to ensure data quality and integrity.
Create and communicate data visualizations and reports to stakeholders, translating technical findings into business recommendations.
Bachelor's degree in Data Science, AI, Statistics, Mathematics, Computer Science, Engineering, or related quantitative field.
3–5 years of professional experience in data science, machine learning, or advanced analytics roles.
Strong proficiency in Python (including Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn) and SQL for data extraction and analysis.
Experience with statistical analysis, hypothesis testing, machine learning models, NLP techniques, and data visualization tools such as Tableau or Preset.
Experienced in applying machine learning and NLP techniques, including Generative AI and Large Language Models, to real-world business problems.
Skilled at collaborating with cross-functional teams to translate business needs into technical solutions and optimize data workflows.
Capable of communicating complex analytical concepts effectively to both technical and non-technical stakeholders.