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# Senior Data Scientist

- **Company:** Fimco Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $83,200.0 - $104,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Python (Programming Language), SAS (Software), SQL Databases, Stata, Snowflake, Data Pipelines
- **Published:** October 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c6b3b97ab635d78f

## About the Role

* A master's degree or Ph.D. in statistics, econometrics, economics, data science, operations research or a related field or in the process of securing one of those degrees.
 * 5+ years of hands-on time-series forecasting experience, ideally in marketing, insurance, banking or another consumer business
 * Strong command of dynamic regression, ARIMA/ETS, hierarchical forecast reconciliation and backtesting
 * Advanced proficiency in Python and/or R, plus SQL; experience with Snowflake, Stata or SAS is a plus
 * The ability to explain model results clearly to business stakeholders
 * Experience working with secure, aggregated client data in regulated industries is a plus, * Independent contractor (1099) engagement
 * Must be authorized to work in the U.S. and able to pass a background check
 * Flexible schedule, with availability for weekly working sessions during business hours (Central Time)

## Description

We're looking for a Senior Data Scientist to lead model development for a sales funnel forecasting system for a leading U.S. financial services and insurance company. You'll build weekly forecasts for each stage of the funnel (traffic, quote, application, join and bind) across six lines of business. The forecasts will roll up to an enterprise forecast of new-member acquisition. You'll work under the direction of our Engagement & Analytics Lead and alongside a Supporting Data Scientist.

What you'll do

 * Lead development of stage-level forecasting models across six lines of business, starting with auto insurance, by customer segment and channel (digital and call center)
 * Build the reusable modeling template (data pipeline, feature library, model code and reporting) that each additional line of business will reuse
 * Develop dynamic regression models with lagged drivers such as marketing spend, pricing, demand signals and economic conditions
 * Model stage-to-stage conversion rates, using logit transformations where appropriate
 * Reconcile forecasts so segment, channel, line-of-business and enterprise totals add up
 * Benchmark models against seasonal naive and exponential smoothing baselines, and run rolling-origin backtests reported as MAPE and MASE with prediction intervals
 * Build the new-member forecasting model from the six line-of-business models
 * Support a simple scenario-planning tool, and help document and train client teams so they can run and extend the system
 * Direct and review the work of the Supporting Data Scientist