Data Scientist

• Dirigir, planificar y controlar el Centro de Excelencia (CoE) de Ingeniería de Data & AI
• Planificar, dirigir y controlar la ejecución integral de proyectos analíticos
• Asegurar la medición, seguimiento y cumplimiento de indicadores de desempeño (KPIs)
• Coordinar con las áreas de negocio y tecnología la adecuada ejecución de los proyectos
• Verificar el cumplimiento de los cronogramas, presupuestos y estándares de calidad establecidos

Senior Data Scientist – Engineering, Data

• Diseña e implementa los modelos analíticos y predictivos que se nutren de la cartera (scoring, alertas, segmentaciones)
• Testing - Backend Automation : Karate
• Testing - Backend Automation : Postman
• Testing - Frontend Automation

• Aportar ideas y conocimientos en soluciones a las necesidades de las personas
• Explorar, analizar y modelar datos de múltiples fuentes y gran volumen
• Usar métodos estadísticos y de machine learning
• Buscar patrones que faciliten la toma de decisiones basadas en datos
• Contarás con el acompañamiento de tu mentor y cursos

• Analyze and derive value from data through the application methods such as mathematics, statistics, computer science, machine learning and data visualization
• Formulate hypotheses and test them using math, statistics, visualization and predictive modeling
• Enable data-driven decision making by creating custom models or prototypes from trends or patterns discerned and by underscoring implications
• Coordinate with other technical/functional teams to implement models and monitor results
• Specify and apply appropriate mathematical, statistical, predictive modelling or machine-learning techniques to analyze data, generate insights, create value and support decision making

Data Science Manager

• Lead the Data and AI Center of Excellence
• Define and prioritize Data and AI initiatives
• Oversee execution of analytical projects
• Ensure compliance with data governance and quality standards
• Collaborate with business and technology teams for project execution

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies.
• Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives.
• Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS.
• Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies.
• Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation.
• Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications.
• Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively.
• Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms.
• Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights.
• Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.

Associate Data Scientist

• Develop, validate, and maintain predictive models including lead scoring, conversion propensity, and funnel optimization.
• Develop tiering models to prioritize leads by conversion probability.
• Build forecasting frameworks for lead volume, conversion rates, and pipeline or revenue projections.
• Build channel performance models to optimize budget allocation across paid search, email, ISO partners, etc.
• Monitor model performance and iterate to improve accuracy and business impact.
• Develop models to evaluate and prioritize markets based on expected performance and ROI.
• Analyze geographic, industry, and segment-level trends to identify growth opportunities.
• Support data-driven recommendations on market targeting and resource allocation.
• Support campaigns/future product launches by forecasting lead volume, conversion rates, and CAC by channel.
• Design and analyze experiments (A/B testing, cohort analysis) and evaluate incremental impact.
• Apply statistical and machine learning techniques to solve marketing and business problems.
• Conduct deep-dive analyses to support strategic and operational decisions.
• Contribute to internal data science and modeling initiatives, including proprietary or internal IP work.
• Collaborate with Data Engineering to structure and prepare datasets for modeling.
• Partner with BI and analytics teams to ensure model outputs are integrated into reporting and usable by stakeholders.

Data Scientist – Marketing Focused

• Partner with the AI Technical Product Manager to support data-driven decision-making across AI initiatives
• Analyze datasets to assess the potential functional and business impact of proposed features
• Define success metrics and measure post-implementation performance to evaluate actual impact
• Provide actionable insights that inform product roadmap, prioritization, and ROI
• Collaborate with ML Engineers to identify machine learning opportunities using proprietary datasets
• Design and execute experiments (A/B testing, hypothesis testing) to validate product and AI feature effectiveness
• Build dashboards, reports, and visualizations to communicate insights to technical and non-technical stakeholders
• Ensure data quality, integrity, and proper usage across projects.

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