Data Scientist at BitGo

Job Description

Key Responsibilities

• Develop and deliver data-driven business insights by analyzing user behavior, market trends, and operational metrics to identify growth opportunities and product optimization strategies.
• Design, implement, and standardize metrics and dashboards that provide real-time visibility into product performance, customer engagement, and business outcomes.
• Collaborate with cross-functional teams, including product managers, engineers, and customer success, to translate data findings into actionable recommendations and solutions.
• Build and automate reporting systems for KPIs across BitGo's diverse product and service offerings, ensuring scalability and consistency in data delivery.
• Partner with product managers to design and execute A/B tests, validate hypotheses, and refine product ideas based on empirical data.
• Work with engineering teams to improve the availability, integrity, accuracy, and reliability of data pipelines, ensuring seamless integration with business tools and platforms.
• Act as a data evangelist by educating stakeholders on data-driven decision-making, fostering a culture of analytics within the organization, and aligning data initiatives with business goals.
• Conduct root-cause analysis on data anomalies and performance bottlenecks to ensure data quality and operational efficiency across all systems.
• Develop predictive models and scenario analyses to forecast business outcomes and support long-term strategic planning.
• Collaborate with marketing and sales teams to analyze customer segmentation, campaign effectiveness, and revenue trends to inform growth strategies.

Job Requirements

• Proven experience in data analysis, preferably in a SaaS or fintech environment, with a focus on product analytics, business intelligence, or growth hacking.
• Advanced proficiency in SQL, Python, or R for data manipulation, analysis, and visualization, with experience in tools like Tableau, Power BI, or Looker.
• Strong understanding of data pipeline architecture, including ETL processes, cloud data platforms (e.g., Snowflake, BigQuery), and data warehousing best practices.
• Excellent communication skills to translate complex data findings into clear, actionable insights for non-technical stakeholders and cross-functional teams.
• Ability to work independently and collaboratively in a fast-paced, dynamic environment with tight deadlines and evolving priorities.
• Experience with agile methodologies and a track record of delivering data-driven projects on time and within scope.
• Knowledge of machine learning techniques and their application to business problems, with experience in model deployment and monitoring.
• Strong analytical mindset with the ability to identify patterns, correlations, and anomalies in large datasets to inform strategic decisions.
• Experience with customer analytics frameworks, including cohort analysis, funnel analysis, and lifetime value (LTV) modeling.
• Excellent problem-solving skills and the ability to think critically about data challenges, proposing innovative solutions to improve data quality and business outcomes.
• Ability to work with stakeholders at all levels, from executives to engineers, to align data initiatives with organizational goals.
• Experience with data governance practices, including data quality standards, metadata management, and compliance with regulatory requirements.
• Strong attention to detail and a commitment to accuracy in data analysis and reporting processes.
• Ability to develop and maintain scalable data systems that support business growth and operational efficiency.
• Experience with data storytelling techniques to present findings in a compelling and actionable manner to decision-makers.