Head of Data & Analytics

Peru

Hybrid

Multinational retail company.

Lead the development and implementation of analytics solutions that support strategic decision-making, including Databricks dashboards, exploratory analysis (SQL, R, Python), and high-impact visualizations.
Manage and coordinate the data analytics team, fostering continuous development and ensuring strong performance at both technical and strategic levels.
Promote a data-driven organizational culture, encouraging the adoption of analytical tools, the use of reliable data, and data-based decision-making across all departments.
Design and develop predictive and prescriptive models using machine learning techniques to anticipate consultant behavior, optimize inventory, and improve the accuracy of sales forecasts.
Implement advanced analytics techniques such as segmentation, scoring, clustering, and propensity models, enabling more personalized and effective strategies.
Translate analytical insights into concrete action plans, collaborating closely with marketing, sales, planning, consultant experience, and Growth teams to generate tangible business impact.
Ensure data quality, integration, and security through robust cleansing and validation processes, in coordination with IT and data architecture teams.
Lead automation initiatives (RPA) and evaluate new tools and technologies that enhance operational efficiency, ensuring alignment with the company’s global data strategy.

Bachelor’s degree in Systems Engineering, Data Science, Statistics, Economics, or related fields.
Master’s degree in Data Science or Advanced Analytics (preferred).
Minimum of 5 years of relevant experience in business intelligence, data analysis, and advanced analytics projects.
Experience in FMCG, banking, telecommunications, or other data-intensive industries.
Proven experience leading technical and multidisciplinary teams.
Strong command of SQL, R, and Python for data analysis and modeling.
Experience developing solutions within the Databricks environment.
Applied data science knowledge, including regression, classification, clustering, decision trees, random forest, etc.
Experience with frameworks such as Scikit-learn, TensorFlow, PyTorch, and MLflow (preferred).
Knowledge of cloud data platforms (AWS, Oracle Cloud, Azure, etc.).
Familiarity with agile methodologies and experimentation (A/B Testing, Test & Learn).