Nayax

Data Scientist

Nayax

full-time

Posted on:

Origin:  • 🇮🇱 Israel

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Job Level

Mid-LevelSenior

Tech Stack

Amazon RedshiftAWSCloudETLNoSQLNumpyPandasPySparkPythonSQL

About the role

  • Own the research agenda across internal and external financial databases; discover signals that drive new revenue and margin expansion
  • Translate ambiguous business questions into crisp analytical plans, metrics, and experiments
  • Query and join data across multiple systems (SQL/NoSQL, data warehouses, APIs); use federated query engines (e.g., Athena/Presto/Trino) where appropriate
  • Build repeatable analysis assets (SQL models, notebooks, lightweight pipelines) and executive-ready artifacts (dashboards, briefs)
  • Partner with Product, Finance, Risk/Credit, Operations and Sales to, set success metrics, and track impact
  • Prototype and apply AI/ML where it matters (e.g., segmentation, propensity, anomaly/fraud signals, pricing experiments) using AWS services (e.g., SageMaker/Bedrock) or equivalent
  • Establish pragmatic data governance: source cataloging, documentation, versioning, code review standards, and data quality checks
  • Champion best practices in privacy/security and compliance in a payments/credit context

Requirements

  • 4+ years in analytics/ data science/ data engineering or quant research roles - must with fintech/payments/credit exposure
  • Preferably in a fintech company or other startup organization focused on B2B financial solutions
  • Strong SQL (window functions, performance tuning) and Python for analysis/ETL (NumPy, pandas/pyarrow; basic PySpark a plus)
  • Comfort with AWS data stack (S3, Glue/Athena, Redshift or Snowflake-on-AWS; SageMaker/Bedrock familiarity preferred)
  • Experience querying across heterogeneous databases and/or federated queries; able to design sane data models and joins across messy sources
  • Proven track record turning analysis into business outcomes (pricing, product, revenue ops, credit/risk insights)
  • Data visualization competency (qliksense, Power BI) and clear writing/presentation for executives
  • Startup mindset: self-directed, bias to action, comfortable with ambiguity; willing to build processes while shipping work
  • Solid engineering hygiene: Git, reproducible notebooks/pipelines, documentation, and basic testing
  • Working knowledge of data governance, privacy, and controls expected in financial services