Analyze, model, and interpret large volumes of data to generate actionable insights that support strategic decision-making, predictive modeling, and a deep understanding of business behaviors, patterns, and trends.

• Explore, clean, and prepare structured and unstructured datasets.
• Design and develop statistical and machine learning models for predictive and descriptive analysis.
• Extract clear and actionable insights from complex datasets.
• Build, test, and validate business hypotheses using data-driven approaches.
• Define metrics, KPIs, and evaluation methodologies.
• Collaborate with AI Engineers to transition models into production environments.
• Communicate findings, insights, and recommendations to both technical and executive stakeholders.
• Support data-driven decision-making across the organization.

Technical Requirements

• Strong background in applied statistics and probability.
• Experience with classical machine learning techniques (regression, classification, clustering).
• Solid experience in Exploratory Data Analysis (EDA).
• Proficiency in Python and/or R for data analysis.
• Hands-on experience with data analysis libraries such as Pandas, NumPy, Scikit-learn, and Statsmodels.
• Experience with data visualization tools and libraries (Matplotlib, Seaborn, Power BI, Tableau, or similar).
• Strong SQL skills for data querying and manipulation.

Experience

• 2+ years of experience in data analysis or applied data science roles.
• Experience working with real-world business data.
• Proven experience presenting analytical results to non-technical stakeholders.

Core Competencies

• Strong critical and analytical thinking skills.
• Ability to tell compelling stories with data.
• High intellectual curiosity and methodological rigor.
• Clear, structured, and effective communication skills.