Algolia is building the next generation of AI powered search products and needs to improve customer and business outcomes through better automated decision-making, using Machine Learning and Statistical Modeling.
Requirements
- Experience in building AI products and familiarity with ML/ methods
- Advanced SQL and Python skills, with a good understanding of best practices in software engineering and data engineering. Familiarity with DBT is a plus.
- Solid knowledge of statistics: hypothesis testing, confidence intervals, and bootstrap. Strong understanding of experimentation methodologies (A/B testing, multivariate testing, etc.) and their application in product decision-making.
- Sound understanding of statistical and machine learning models: gradient boosted trees, logistic regression, neural networks, survival analysis, etc.
- Familiarity with end-to-end model development and maintenance of ML models used for business-critical decisions.
- Solid understanding of key product metrics such as user engagement, retention, churn, lifetime value, and conversion rates.
- Familiarity with search engines and search technologies.
Responsibilities
- Generate actionable product insights: Analyse and aggregate behaviour data to highlight trends, surface friction points and uncover opportunities that improve engagement, retention and conversion by translating findings into clear product recommendations that influence sprint planning and feature prioritisation.
- Design, run and evaluate experiments: Own end-to-end A/B test design, from hypothesis generation and power analysis through to post-test read-outs by partnering with engineering to ensure robust experiment implementation and with product to interpret results and iterate quickly.
- Define and monitor core product metrics: Define, track, and optimise product KPIs (such as user engagement, retention, conversion rates, etc.) to ensure that product improvements are measurable and aligned with business objectives via automated dashboards to make performance visible to teams and leadership by collaborating with Product Managers.
- User Segmentation & Cohort Analysis: Conduct deep-dive analyses into user cohorts and segments to identify patterns in behaviour and tailor product strategies to different user groups, and recommend tailored product strategies for high-value or at-risk cohorts
- Data Quality & Integrity: Validate tracking implementation, debug data issues and establish checks that guarantee analytical accuracy, and maintain well-documented code, queries and analytical notebooks.
- Contribute to scalable analytics infrastructure: Collaborate with Analytics Engineering to evolve datasets, schemas and pipelines that support self-serve analysis, and develop reusable modelling templates and experiment analysis frameworks.
- Documentation & Knowledge Sharing: Maintain clear documentation of product analytics methodologies, metrics, and insights, facilitating knowledge sharing across product and engineering teams, and craft compelling data stories using visualisations, clear narrative and business context.
Other
- Spend 1-2 days per week in a local coworking space to collaborate with your teammates in person.
- 3+ years of hands-on experience in product analytics or applied data science, ideally within consumer-facing digital products.
- Ability to work with engineering, design, product marketing, GTM sales, and customer support to help launch new products and support existing ones.
- Ability to understand technical business problems, craft effective strategies to tackle them, and present simple solutions to customers.
- Detail-oriented, with a focus on data quality and integrity. Great attention to detail while keeping an eye on the big picture.
