Location: Full remote.
Schedule: Full-time, European time zone availability.
Job Purpose
We are looking for a Data Analyst whose primary mission is to turn data into actionable insights and deepen our understanding of both the Italian market and the broader classified industry. In this role, you will dissect user behavior, evaluate strategic initiatives, and experiment with new product features. Your work will have a direct impact on the growth of our business.
Key Responsibilities
- Central Role in Data-Driven Product Design: Collaborate with product managers and cross-functional teams to inform and influence design decisions through data.
- Analyze Diverse Data Sets: Leverage our data lake and engineering support to generate high-value insights that guide business strategies.
- Formulate Hypotheses & Test Them: Set up robust experiments or A/B tests to validate hypotheses and ensure data-driven decision-making.
- Create Automated Reports: Develop clear and dynamic dashboards to democratize data access across the entire organization.
- Contribute to Machine Learning Initiatives: Dig deeper into the data lake to develop and refine ML models, making our product smarter and more personalized.
Experience & Qualifications
- 3+ years of experience in a Data Analyst
- High proficiency in data extraction tools, particularly SQL and Spark, for handling large datasets.
- Skilled with key libraries (pandas, NumPy, matplotlib) and rapid prototyping tools (Streamlit).
- Strong skills in an analytics language Python or R for data manipulation and statistical analysis.
- SQL & Spark: Experienced in writing complex queries, handling large datasets, and leveraging Spark for distributed data processing.
- Proficient in Tableau or similar platforms, capable of crafting clear, impactful dashboards for diverse audiences.
- Mastery of experiment design, hypothesis testing, and both descriptive and inferential statistical methods.
- Ability to align analytical findings with strategic goals, prioritizing insights that drive meaningful outcomes.
- Self-directed and proactive in managing projects, setting priorities, and meeting deadlines.
- Applied statistical techniques knowledge to build robust analyses and interpret findings.
- Excellent data visualization and presentation abilities to communicate insights to both technical and non-technical audiences.
- Advanced English.
Nice to Have
- Designing, Training, and Deploying ML Models: Some hands-on experience across the full ML lifecycle, from data prep to model deployment.
- ML Ops Tools: Basic familiarity with platforms like Kubeflow or MLflow for model tracking and lifecycle management.
- Git: Foundational understanding of version control workflows, including branching and pull requests.
