dear candidate,
straive is hiring for senior data scientist - pricing & promotion analytics interested candidate can apply to below link:-
job description:-
we are seeking a highly skilled and experienced lead data scientist to join our pricing & promotion
analytics team. this role is focused on solving high-impact commercial problems in the consumer
packaged goods (cpg) space, including trade promotion optimization, price elasticity modeling,
promotion response analytics, and assortment optimization.
the ideal candidate will bring deep technical expertise, strong business acumen, and hands-on
experience working with syndicated retail data (nielsen, iri, etc.) and advanced modeling
techniques. this role also offers the opportunity to contribute to the development and
enhancement of pricing and promotion optimization tools.
key responsibilities
- design and implement advanced models to solve business problems such as:
- trade promotion optimization (tpo)
- price elasticity modeling
- promotion response estimation
- assortment optimization
apply a range of statistical and machine learning techniques, including but not limited to:
- linear and non-linear regression
- hierarchical bayesian regression (hbr)
- mixed-effects models
- optimization algorithms (e.g., linear, non-linear, integer programming)
collaborate on the development and enhancement of pricing and promotion optimization tools, contributing algorithms and technical logic.
- leverage syndicated pos data (e.g., nielsen, iri) and internal client data to develop robust,
production-grade models.
- translate analytical outputs into business insights and actionable recommendations for pricing, promotion planning, and assortment strategy.
- work cross-functionally with business, tech, and product teams to embed models into
enterprise systems and business workflows.
- provide technical mentorship to junior data scientists and contribute to the development of modeling standards and best practices.
required skills
- 5+ years of hands-on experience in data science or applied analytics
- strong understanding of cpg commercial functions: pricing, promotions, rgm, category management
- statistical modeling: linear/multiple regression, logistic regression, hierarchical bayesian regression (hbr), time-series forecasting
- optimization techniques: linear programming (lp), mixed-integer programming (mip),
constraint-based modeling, gradient-based optimization
- programming languages: proficiency in python or r (preferred libraries: statsmodels, pymc3, scikit-learn, pandas, numpy)
- sql: strong data manipulation and querying skills
- data visualization: experience with tools such as tableau, power bi, or python/r-based
libraries like matplotlib, seaborn, plotly
- cloud & tooling (optional but preferred): exposure to cloud platforms (aws, gcp, azure),
git, docker, ci/cd pipelines
- cpg data handling: experience working with large-scale retail data such as nielsen, iri, retailer pos systems; comfort with cleaning, transforming, and harmonizing disparate data sets
- strong business communication skills; ability to explain technical concepts to non-technical stakeholders
- good to have prior consulting experience
