Data Scientist, FP&A Solutions

Autodesk•Portland, OR

5d•Onsite

About The Position

We are seeking a Data Scientist to join our growing FP&A Solutions team within the Finance Transformation organization. This role sits at the intersection of finance, data science, and analytics engineering. The Data Scientist will partner closely with FP&A, Finance, Data Engineering, and business teams to automate financial analysis, develop forecasting and predictive models, build scalable data solutions, and improve the quality and speed of financial decision-making. The ideal candidate combines strong technical skills with financial acumen and can translate complex data into clear recommendations for business and finance leaders.

Requirements

5+ years of experience in data science, advanced analytics, financial analytics, FP&A analytics, or a related field
Advanced proficiency in Python, including experience with libraries such as pandas, NumPy, scikit-learn, statsmodels, or similar analytical frameworks
Advanced SQL skills and hands-on experience working with Snowflake
Strong understanding of FP&A concepts, including budgeting, forecasting, variance analysis, financial modeling, management reporting, and scenario planning
Experience developing statistical, predictive, or machine-learning models against large financial or operational datasets
Experience building automated and reproducible analytical workflows
Strong understanding of data modeling, data transformation, and analytical data structures
Ability to evaluate data quality and identify inconsistencies, anomalies, and underlying business drivers
Strong communication skills with the ability to explain technical concepts and analytical findings to non-technical audiences
Demonstrated ability to work across Finance, Data, Technology, and business organizations
Bachelor's or master's degree in Data Science, Statistics, Computer Science, Economics, Finance, Mathematics, Engineering, or a related quantitative discipline, or equivalent practical experience

Nice To Haves

Experience working directly within or closely supporting a corporate FP&A organization
Experience with financial forecasting, time-series analysis, driver-based planning, Monte Carlo simulation, or scenario modeling
Experience developing production-quality analytical solutions using Snowflake and Python
Familiarity with Snowpark for Python, dbt, orchestration tools, or modern data-engineering practices
Experience with financial planning platforms such as Adaptive Planning, Anaplan, Oracle EPM, or similar systems
Experience with visualization and BI platforms such as Power BI, Tableau, or Looker
Experience applying machine learning, generative AI, or intelligent automation to Finance use cases
Familiarity with software development practices including Git, testing, CI/CD, code review, and version control
Experience working in a large, global, or matrixed organization

Responsibilities

Partner with FP&A and business leaders to identify opportunities where data science and advanced analytics can improve planning, forecasting, and decision-making
Develop Python-based analytical models, forecasting solutions, simulations, and automation for financial and operational use cases
Build and maintain scalable datasets and analytical workflows using Snowflake, SQL, and Python
Develop predictive models for areas such as revenue, expenses, headcount, bookings, customer behavior, cash flow, and business performance
Improve financial forecasting through statistical modeling, machine learning, driver-based forecasting, and scenario analysis
Automate recurring FP&A processes, including data preparation, variance analysis, forecast updates, management reporting, and financial performance analysis
Perform financial and operational variance analysis to identify key drivers, trends, anomalies, and emerging risks
Design scenario and sensitivity models that help leadership understand potential financial outcomes and tradeoffs
Integrate financial and operational data from multiple systems into trusted analytical datasets within Snowflake
Develop reusable Python libraries, notebooks, pipelines, and analytical frameworks that improve productivity across Finance and Analytics teams
Partner with Data Engineering teams to improve data quality, architecture, governance, and performance
Translate complex analyses and model outputs into concise insights and recommendations for finance and business stakeholders
Establish appropriate model validation, monitoring, documentation, and data-quality controls
Promote adoption of data science, automation, and AI capabilities within the Finance organization