About the Role
We are looking for a high-agency Data Analyst with 3–5 years of overall experience, including relevant internships, who can use data, experimentation and AI to influence business decisions.
This role is suited for someone who has worked in a fast-paced, high-outcome environment and is comfortable moving from an ambiguous business question to analysis, recommendation and implementation. The analyst will work closely with product, business, engineering and other functional teams to identify opportunities, evaluate solutions and drive measurable outcomes. Note that the role requires overlap specifically for the first two hours of business everyday with the team in the MT zone.
Key Responsibilities
Partner with business and product teams to translate open-ended problems into clear analytical questions, measurable hypotheses and actionable recommendations.
Analyse customer journeys, funnels, engagement, and retention to identify growth opportunities.
Conduct root-cause analyses to explain changes in business metrics and recommend corrective actions.
Design and evaluate experiments with appropriate statistical and business context.
Develop dashboards, automated reporting and analytical pipelines that enable reliable decision-making.
Build or contribute to AI-enabled analytics solutions or decision-making tools.
Collaborate with data engineering teams to improve data definitions, analytical datasets, metric consistency and data quality.
Present findings to business stakeholders with storytelling and ability to zoom-out appropriately.
You Have:
1–3 years of relevant experience across full-time roles and internships in data analytics, product analytics, data science or a related field.
Strong working knowledge of SQL and the ability to independently analyse large & complex datasets.
Working proficiency in Python for analysis, automation or model development.
Demonstrated experience delivering an AI or analytics solution that influenced a business decision, improved a process or enabled stakeholder self-service.
Experience working with business metrics and processes with the corresponding analytics frameworks.
Ability to build scalable analytical outputs rather than repeatedly solving the same problem through manual analysis and/or dashboards.
Familiarity with data platforms such as Tableau, Snowflake, Databricks or comparable tools.
Bonus Points if You Have:
Prior exposure to machine-learning models, including data preparation, feature engineering, model validation and performance evaluation.
Exposure to fintech, SaaS, consumer technology or digital-product environments. • Experience working directly with stakeholders like product managers, business leaders, engineers or operations teams.
Evidence of strong analytical work through courses, publications or open-source contributions.
Behavioural Strengths
Self-starter with a bias to action: Takes ownership, moves forward despite ambiguity and does not wait for perfectly defined requirements.
Outcome orientation: Connects analytical work to measurable business or customer impact.
Curiosity: Goes beyond the immediate request to understand why a metric moved and what decision should follow.
Strive for excellence: Continuously improves the quality, scalability and usefulness of analytical solutions.
Attention to detail: Validates data, assumptions, definitions and results before presenting conclusions.
Structured problem-solving: Breaks broad business problems into testable components and prioritises the analyses that matter most.
Clear communication: Explains complex findings simply and adapts the level of detail to the audience.
Collaborative ownership: Works effectively across functions while remaining personally accountable for delivery.
Learning agility: Quickly develops working knowledge of new tools, business domains and analytical methods.
What Success Looks Like:
Independently own analytical problems from problem definition through recommendation.
Build trusted relationships with business and product stakeholders.
Identify and deliver at least one measurable business improvement from analytics, experimentation, AI.
Reduce recurring manual analysis through automation or reusable analytical assets.
Improve the velocity and quality of decision-making by presenting clear, evidence-based recommendations.
Within the first few months, the successful candidate should be able to:
