Job description
At Finyard, we’re a global team of engineers, data scientists, marketeers, and financial experts, passionate about technology and innovation. We’re all about bringing revolutionary software services to people all around the world, and have been since 2018.
Our mission is to innovate by launching modern software solutions in the FinTech space, giving users around the world simpler and quicker ways to transact and manage their investments. We are committed to ensuring every product we release is in service of our users, so that as we grow, so do they.
We are looking for a Data Analyst to join our Data Office and be allocated to the Product domain. You will partner closely with Product, Commercial, and Engineering teams to understand user behaviour, evaluate and generate product hypotheses, design and analyse experiments, improve key business and product metrics, and build scalable analytics foundations such as metrics, data marts, dashboards, and alerting.
Key responsibilities
Product analytics & insights
Analyse user behaviour and product journeys across web and mobile applications: funnels, activation, engagement, retention, monetisation, and feature adoption.
Identify drop-offs, behavioural patterns, friction points, and growth opportunities and translate them into actionable product hypotheses.
Evaluate the impact of product launches and changes and explain what changed, why it changed, and for whom.
Experimentation & hypotheses
Work with the Product team to formulate and prioritise hypotheses and define measurable success criteria.
Design and analyse A/B tests and other experiments, including primary metrics, guardrails, sample-size considerations, segmentation, and statistical interpretation.
Ensure experiment results are translated into clear product decisions and next steps, not just statistical conclusions.
Metrics & product monitoring
Define and maintain product KPIs and metric trees, ensuring consistent definitions across teams.
Develop dashboards and analytical views that help Product teams understand performance without relying on manual analysis.
Segmentation & behavioural research
Build meaningful user and behavioural segmentations based on lifecycle stage, product usage, engagement, monetisation, and other relevant characteristics.
Analyse differences between user cohorts and segments to understand which product experiences work for whom.
Data foundations & analytics quality
Formulate analytical and business requirements and actively participate in building product data marts and single-source-of-truth datasets.
Partner with Engineering and Data Engineering on event tracking and instrumentation, ensuring new features generate reliable and useful analytical data.
Improve the product metrics system, including definitions, documentation, ownership, and consistency.
What we expect (must-have)
3+ years of experience in data analytics (fintech/product experience is a strong plus).
Strong SQL+Python and hands-on experience working with large datasets.
Strong understanding of product analytics concepts: funnels, conversion, retention, cohorts, engagement, monetisation, and segmentation.
Hands-on experience with A/B testing and experiment analysis, including statistical significance, confidence intervals, guardrail metrics, and common sources of bias.
Experience with product analytics tooling and event-based tracking.
Ability to communicate insights clearly and collaborate with cross-functional stakeholders.
Advanced Russian and English
Nice-to-have
Experience building analytical data marts or working with dbt-style analytics engineering workflows.
Experience owning event tracking across web and mobile applications.
Understanding of marketing analytics, acquisition, and attribution.
Our technological stack
Databases: Snowflake, ClickHouse, MySQL, BigQuery
Visualisation systems: Tableau, HEX
Analytics tools: Amplitude
Programming language: Python (pandas, numpy, scikit-learn)
Interactive environments: Jupyter Notebook, HEX
Originally posted on Himalayas
Who can apply
The source lists worldwide eligibility. Accepted UTC offsets: UTC-11, UTC-10, UTC-9.5, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC-4, UTC-3.5, UTC-3, UTC-2, UTC-1, UTC+0, UTC+1, UTC+2, UTC+3, UTC+3.5, UTC+4, UTC+4.5, UTC+5, UTC+5.5, UTC+5.75, UTC+6, UTC+6.5, UTC+7, UTC+8, UTC+8.75, UTC+9, UTC+9.5, UTC+10, UTC+10.5, UTC+11, UTC+12, UTC+12.75, UTC+13, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.