200+ applicants
Kimberly-Clark · 3 days ago
Data Prescriptive Scientist
Chicago, IL
Full-time
Hybrid
Mid Level
3+ years exp
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Responsibilities
Work with Data Science Strategy Leader to define data science use cases with a bias towards problems requiring prescriptive analytics, propose potential modeling approaches, assess feasibility, estimate effort and data requirements, draft project plans.
Build Proof-of-Concept data science models: acquire, cleanse, and harmonize data, analyze, and identify appropriate optimization algorithms, build models that are interpretable, explainable, and sustainable at scale and meet the business needs.
Engage stakeholders, including product and business teams, through frequent check-ins, progress updates, visualizations, and interactive dashboards designed for non-technical audiences.
Interpret model outputs, draw actionable insights, present findings, and make recommendations to cross-functional and senior leadership teams.
Collaborate with Enterprise AI/ML product teams and DTS Delivery teams to scale proven high-value Proof-of-Concept models where enterprise certified products are required.
Qualification
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Required
3+ years of experience with hands-on involvement in data science projects (research or consulting experience is a plus; experience in CPG/Retail, Digital Marketing, e-Commerce, or Revenue Management preferred)
A bachelor’s degree in a quantitative field (e.g., Engineering, Computer Science, Statistics, Economics, or Mathematics) - An advanced degree in a quantitative field is preferred and will be accepted in lieu of 3+ years of experience.
Proficiency in SQL and any one other programming language (e.g., R, Python, C++, Minitab, SAS, Matlab, VBA – knowledge of optimization engines such as CPLEX or Gurobi is a plus)
Proficiency in any data visualization software (e.g., Power BI, Tableau, Qlik, D3, Shiny)
Theoretical or practical experience in mathematical optimization techniques ( e.g. linear and non-linear optimization, mixed integer programming, sensitivity analysis, constraint programming etc.)
Experience in machine learning methods (e.g. multivariate regression, feature engineering, random forests, XGBoost, elastic nets, hierarchical bayesian regression, unsupervised learning, clustering/segmentation)
Preferred
Experience grounding muddy and fuzzy analytics problems with an open, inquisitive mind to seek information and challenge the status quo.
Comfort in making reasonable assumptions to push forward when faced with less than complete data, known and unknown unknowns, in a fail-fast fail-forward environment.
Very strong academic curiosity to research and learn new approaches, thrive in a culture of innovation, be opened to challenge, and be challenged.
Self-starter who can see the big picture, look ahead to identify opportunities, and prioritize your work to make the largest impact on the businesses and customer’s vision and requirements.
Benefits
Medical, dental, and vision coverage options with no waiting periods or pre-existing condition restrictions
Access to an on-site fitness center
Occupational health nurse
Allowances for high-quality safety equipment
Flexible Savings and spending accounts
Diverse income protection insurance options
Additional programs and support for education, adoption, relocation, and temporary childcare
Company
Kimberly-Clark
Kimberly-Clark ranked among the top 10 World's Best Multinational Workplaces by Great Places to Work Institute for the third year in a row.
10,001+ employees
H1B Sponsorship
Kimberly-Clark has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Trends of Total Sponsorships
2023 (22)
2022 (30)
2021 (8)
2020 (10)
Funding
Current Stage
Late StageLeadership Team
C
Carl T. Davis
CFO North America Personal Care
M
Mark Riordan
Chief Financial Officer
Recent News
2024-05-24
2024-05-24
Company data provided by crunchbase
