Data Aalyst

Description:

About BioCatch

BioCatch is the leader in Behavioral Biometrics, a technology that leverages machine learning to analyze an online user’s physical and cognitive digital behavior to protect individuals online. BioCatch’s mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust, and ease coexist.Today, 32 of the world's largest 100 banks and 210 total financial institutions rely on BioCatch Connect™ to combat fraud, facilitate digital transformation, and grow customer relationships.. BioCatch’s Client Innovation Board, an industry-led initiative including American Express, Barclays, Citi Ventures, and National Australia Bank, helps BioCatch to identify creative and cutting-edge ways to leverage the unique attributes of behavior for fraud prevention. With over a decade of analyzing data, more than 80 registered patents, and unparalleled experience, BioCatch continues to innovate to solve tomorrow’s problems. For more information, please visit www.biocatch.com. 

About the Role

We are looking for a highly analytical Data Analyst to join our Data Science team and support the development, evaluation, and improvement of data-driven models and research initiatives. In this role, you will work closely with data scientists to analyze large-scale behavioral and transactional datasets, generate insights, and help translate raw data into meaningful signals.

Responsibilities

• Conduct exploratory analysis on large-scale behavioral and transactional datasets.
• Partner with data scientists to support model development, evaluation, and validation activities.
• Investigate patterns, anomalies, and trends in user behavior, fraud indicators, and system performance.
• Build Python-based analytical workflows to support data investigations and research tasks.
• Collaborate with engineering and data teams to maintain high standards of data quality and integrity.
• Communicate analytical findings clearly and concisely to the data science team.

• 2–4 years of experience in a Data Analyst or similar analytical role
• Strong Python skills for data analysis (e.g., pandas, numpy, data exploration)
• Experience using AI-assisted tools (e.g., generative AI, code assistants, or data analysis copilots) to enhance data exploration, analysis efficiency, and insight generation, with an understanding of their limitations and best practices
• Strong SQL skills and experience working with large datasets
• Solid understanding of statistical concepts and exploratory data analysis techniques
• Strong problem-solving and analytical thinking skills
• Ability to clearly communicate analytical findings
• Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field

Nice to Have

• Experience working closely with data science or machine learning teams
• Familiarity with machine learning workflows and model evaluation
• Experience in fraud detection, risk, cybersecurity, or fintech domains
• Experience working with large-scale data pipelines.