Lead Data Scientist

Fieldguide•San Francisco, CA

22h

About The Position

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.

Requirements

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

Nice To Haves

Familiarity with prototyping and deploying machine learning models
Familiarity with deep learning, natural language processing, and generative AI techniques

Responsibilities

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