Lead Data Scientist, Fieldguide

• Minimum 5+ years of experience as a Data Scientist, Data Analyst, or similar role working with large and complex datasets
• Strong SQL skills; proficiency in Python (e.g., pandas, NumPy)
• Have experience shipping production code for customer-facing products
• Experience with modern data tooling such as dbt, Airflow, or equivalent orchestration and transformation tools
• Hands-on experience with data visualization tools (e.g., Tableau, Power BI, Looker) to communicate insights effectively
• Technical expertise in building scalable data models, ETL pipelines, and designing data architecture
• Business acumen with the ability to translate data into strategic insights
• Excellent problem-solving, collaboration, and communication skills
• BSc/BA in Computer Science, Engineering, Math/Statistics, Business, or relevant field; graduate degree in quantitative field is preferred

Desirable

• The Data Science team at Fieldguide leverages novel, proprietary datasets to build Fieldguide Insights – a product that delivers new-to-the-industry visibility into Audit and Advisory execution and performance
• You'll work closely with product, design, engineering, and customer teams to build analytics and data science models that uncover key performance drivers, identify operational inefficiencies, and enable customers to make informed, data-driven decisions
• This role is ideal for someone who thrives in solving complex, ambiguous problems and is excited about shaping the future of intelligent workflows for Audit and Advisory firms
• Analyze large volumes of structured and unstructured data to uncover trends, patterns, and actionable insights
• Define and track key metrics to evaluate firm, team, and engagement performance using Fieldguide’s industry-leading dataset
• Design and build customer-facing analytics features and dashboards that deliver measurable value by uncovering opportunities and supporting strategic decision-making
• Build and maintain scalable data infrastructure—including preprocessing, ETL pipelines, data modeling, orchestration, and monitoring—to support analytics and product insights
• Lead efforts to improve data quality, completeness, and accessibility across internal systems and product experiences