Product Analyst managing the evolution of digital sports products like Cartola and ge. Collaborating across teams to enhance user experience and monetization strategies.
About the role
- Head of Data Science at Varo leveraging analytics and machine learning to drive growth. Leading a team to solve complex business challenges in financial services.
Responsibilities
- Strategic Ownership: Define, own, and drive the comprehensive Data Science roadmap (including AI/ML and causal inference) that directly translates into commercial outcomes across Varo’s core business verticals.
- Model Innovation & Delivery: Lead the development, validation, and production deployment of custom machine learning and statistical models that are critical for decision-making.
- Business Partnership: Serve as a strategic data consultant to executive leaders and business stakeholders, translating ambiguous commercial problems into rigorous analytical frameworks and actionable solutions.
- Data Integrity & Risk: Work closely with Data Engineering and Product teams to ensure the quality, accessibility, and lineage of new data sources. Ensure all models and analyses adhere to Varo’s risk framework and regulatory requirements for fairness and transparency.
- Operational Excellence: Establish and own the ML Model Performance Monitoring processes, ensuring deployed models maintain accuracy and business impact over time and are governed appropriately.
- Team Leadership & Mentorship: Attract, hire, mentor, and coach a high-performing team of Data Scientists, setting a high bar for technical rigor, business acumen, and cross-functional collaboration.
Requirements
- 8+ years of experience in Data Science, Applied Science, or a related quantitative field.
- Deep Domain Expertise: Proven track record of delivering measurable business impact in at least one key financial services domain (e.g., credit/lending, fraud/risk modeling, or customer growth/engagement).
- Technical Fluency: Expert-level proficiency in Python and its scientific computing stack (Pandas, Scikit-learn, PyTorch/TensorFlow). Proven ability to apply a wide range of statistical methods, machine learning algorithms, and causal inference techniques to large, real-world datasets.
- Data Ecosystem Experience: Hands-on experience working with distributed data and computing tools (e.g., Spark, Hive) and cloud web services (e.g., AWS, GCP, or Azure).
- Leadership Acumen: Demonstrated ability to thrive in a fast-paced environment, attract high-quality talent, and drive a data-informed culture.
Benefits
- This role is also eligible for a bonus
- equity
- competitive benefits
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