Data Scientist

• Lead product management and the learning engineering tribe. Manage A/B testing pipelines and hypothesis validation to scale evidence-based products.
• Define strategies and deliver personalized instructional interventions.
• Manage cross-functional teams, promoting development and high performance.
• Plan and coordinate projects using agile methodologies to deliver innovative solutions.
• Perform critical data analysis.
• Create storytelling and executive reports based on metrics and data.
• Apply statistical analysis techniques and advanced algorithms to enhance academic digital products.
• Communicate effectively with executive stakeholders, influencing strategic decisions.
• Foster a culture of continuous learning and innovation within the team.

Data Product Manager

• Senior professional with strong analytical skills and business acumen, responsible for understanding processes, identifying value-creation opportunities, and translating complex challenges into analytical solutions.
• Will act as a strategic bridge between business areas and the technical team, designing and prioritizing data-driven initiatives based on their potential to generate value.
• Facilitation and immersion: lead meetings to gather requirements, map and model processes with clients;
• Solution formulation: translate client needs and challenges into analytical approaches, defining modeling assumptions and the most suitable method;
• Analytics liaison: translate business challenges for the data science team, prioritizing the highest-impact initiatives;
• Storytelling and executive presentation: lead presentations to client senior management, turning complex data and BI reports into clear, actionable insights;
• Strategy and value generation: define metrics (KPIs), build structured business cases, and quantify the financial/strategic return of proposed solutions.

Senior Data Science Consultant

• Must be able to lead an entire project with minimal supervision, designing and building mathematical/statistical solutions according to business requirements.
• From the initial meeting, define the project scope with the business area, translating requirements into programmatic statistical models.
• Analyze timelines and trade-offs between techniques and business needs; implement all project stages (data collection and analysis, formatting for model ingestion, modeling, and generation of usable outputs).
• Must also be able to guide and teach any part of the machine learning project lifecycle to less experienced team members.
• Liaise with the business area regarding any needs during the project.
• Development of transactional chatbot models.
• Create AI models to apply rules for identifying transactional fraud.
• Correlate datasets.
• Create dashboards to visualize the key established metrics.
• Document the development technically.

Data Scientist jobs in Brasil

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Data Product Manager

Senior Data Science Consultant

Senior Data Scientist, Chatbot, AI