We are seeking an inquisitive and impact-driven Data Scientist to join our Analytics team. In this role, you won't just build models—you will translate complex, unstructured data into actionable strategic directions that drive our core business forward. You will partner closely with Product, Engineering, Marketing, and Operations to build predictive frameworks, and discover growth opportunities.
.
Key Responsibilities
Experimentation & A/B Testing: Design, execute, and analyze rigorous A/B and multivariate tests to measure feature performance, user retention, and business impact.
Predictive Analytics & Machine Learning: Build, validate, and maintain scalable predictive models (e.g., churn, forecasting, customer lifetime value (CLV) and user segmentation) to guide business decisions.
Exploratory Data Analysis: Deep dive into large, complex datasets to identify trends, user behavioral patterns, operational bottlenecks, and unexpected growth opportunities.
Features Definition & Measurement: Establish and standardize key features to track for modelling and customer data platform .
Cross-Functional Strategy: Partner with product managers, executive leadership, and business stakeholders to turn open-ended questions into structured quantitative analyses.
Analytics AI Agents: Design, deploy, and manage autonomous AI agents that automate repetitive analytics tasks and generate insights.
Data Pipelines & Quality Assurance: Partner with Data Engineering to curate analytical datasets, build clean data transformations (dataform/SQL), and uphold data governance standard practices.
Qualifications & Skills
Technical Competencies
Advanced SQL: Highly proficient in writing optimized, complex queries across large cloud data warehouses (e.g., Snowflake, BigQuery).
Programming & Modelling: Strong proficiency in Python for statistical modeling, forecasting, and machine learning.
Statistics & Math: Deep understanding of statistical inference, regression analysis, probability theory, decision trees, and hypothesis testing (p-values, confidence intervals, Bayesian testing etc.).
Data Visualization: Hands-on experience building interactive dashboards using tools like Power BI, Looker, or Python libraries.
Experience & Soft Skills
Experience: 3+ years of experience in a quantitative analytics or data science role in a fast-paced environment.
Business Acumen: Demonstrated track record of framing business problems into mathematical/statistical frameworks and outputting actionable recommendations.
Communication: Ability to distill complex statistical concepts into clear, plain language for non-technical executives.
Nice-to-Haves
Experience with data building tools (i.e. Dataform) for data transformation workflows.
Experience with digital product analytics tools (i.e., Google Analytics).
Experience with AI Agents i.e, Vertex AI (Gemini Enterprise Agent Platform)
.png)
