Senior Data Engineer / Data Scientist

RESPONSIBILITIES:
Kforce has a client in Saint Louis, MO that is seeking a Senior Data Engineer/Data Scientist.

Responsibilities:
* Build Python Pipelines for Promo + POS + Trade Spend Data
* Develop and maintain Python scripts to ingest and standardize datasets such as: Syndicated POS (e.g., Circana or equivalent), internal shipment/sales, promo calendars, pricing, and trade spend
* Create durable, reusable data preparation code (feature engineering, calendar alignment, store/customer/SKU hierarchy mapping)
* Produce clean, analysis-ready datasets to support portfolio impact modeling
* Build repeatable Python workflows that can run by customer/channel/SKU group and produce comparable outputs across time periods
* Create -explainability- artifacts (assumptions, controls, sensitivity checks) so results are trusted
* Deliver decomposition views that separate -true incrementality- from mix effects
* Produce standardized tables/data products for downstream BI and reporting (Power BI, Excel packs, shared datasets)
* Automate refresh and re-runs with parameterized scripts (by customer, date range, promotion, portfolio slice)
* Partner with BI/analytics teams to make outputs consumable and scalable
* Validate model outputs against known benchmarks and actuals; diagnose outliers and data anomalies
* Communicate results clearly (what happened, why, and confidence/limitations), but with a builder's mindset
* Document code, data assumptions, and run instructions to enable repeatability and handoff

Implement methods to estimate:
* Halo lift on related SKbrands/categories during promoted periods
* Cannibalization within portfolio (substitution from non-promoted SKUs to promoted SKUs)
* Net portfolio incrementality vs. share shifts

Analyze how promotions shift volume across:
* Channels (e.g., mass, grocery, club, eComm where applicable)
* Customers/accounts
* Pack types, price tiers, and assortment groupings

REQUIREMENTS:
* 5-10+ years in analytics roles supporting trade promotion, pricing, revenue management, category management, or CPG/Retail analytics
* Strong Python skills with evidence of building repeatable analytics (not just ad-hoc notebooks).
pandas/numpy, statistical modeling, time series alignment, feature engineering
* Experience working with promo calendars + POS/sales data (syndicated or retailer feeds) and deriving promotion lift/incrementality insights
* Comfort operating end-to-end: ambiguous data, clean dataset, method, results, repeatable script

Preferred Qualifications:
* Experience implementing incrementality approaches (e.g., pre/post with controls, matched-market style controls, regression-based lift, hierarchical rollups)
* SQL proficiency for extracting/validating data
* Familiarity with modern analytics platforms (Databricks, cloud data lakes) and BI integration patterns

The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.

We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.

Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.

This job is not eligible for bonuses, incentives or commissions.

Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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