Data Scientist

Description:

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 meets 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

Critical Soft Skills:

Effective, clear and compelling communication skills to engage stakeholders and maintain their engagement throughout the process

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

Critical Technical Skills:

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 2 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)