Decision Science AnalystAs a Decision Science Analyst, you'll extract, explore, validate, and cleanse data using advanced coding techniques. You'll design and apply analytical and statistical models to segment, describe, and understand our customers and their purchasing habits and proclivities. You'll serve as a strategic thought partner, providing perspective and recommendations to solve business challenges.

Key responsibilities include

Design & Development / Analytics: Using coding techniques (predominately SQL and Python), aggregates billions of data records to measure customer behavior changes in multiple dimensions (e.g., customer count and value, frequency of purchase, average purchase size, ROI). Mines / validates / cleanses data Works across several data platforms (e.g., clickstream, web analytics, transactional data, unaggregated customer data) Applies advanced analytical and statistical models to segment / understand our customer (e.g., k-means clustering, a/b testing) Applies experience, knowledge, intuition to dig for insights to share through enterprise tools (e.g., Tableau, MicroStrategy) Asks questions to identify deeper reasoning behind business questions Translates / tests / connects data findings into actionable business insights Provides customer insights and in-depth analytics on marketing initiatives (e.g., pre- / post-campaign) Develops / deploys experiments and data science models to measure customer loyalty, habits, tendencies, affinities, and propensities to reliably predict behavior and prescribe initiatives, strategies, tactics Provides ad hoc analysis upon request

Advising: Serves as a strategic thought partner / team member of assigned team; provides perspective on business challenges Serves as the data-expert in cross-functional groups to optimize H-E-B marketing investments and assortment decisions Provides day-to-day broad visibility into customer learnings and impact through ongoing performance dashboards / metrics Summarizes for / shares insights with business stakeholders, informally and in formal presentations Aligns with executive-level stakeholders on prioritizing initiatives Leads projects Mentors / coaches

Qualifications & Key

Requirements

Work

Experience

Expertise in business domain of relevant experience - preferably in retail / other consumer-facing industry Experience in statistical programming languages (R

• Python
• SQL) Experience in relational databases such as Teradata Oracle and big data platforms Experience in data extraction, cleansing, validating and curating

Knowledge/Skills/Abilities: Working knowledge of typical data science techniques (e.g., classification, regression, and optimization) Strong working understanding of data exploration, applying analytical tools, and how to advise business stakeholders Familiarity with big data ecosystems (Databricks, Spark, etc.), relational DBs, SQL, business intelligence and visualization tools (Tableau or MicroStrategy, etc.) Strong research and analytical skills Strong critical and lateral thinking skills Verbal / written communication and presentation skills Mentoring / coaching skills Ability to lead projects Ability to turn data into actionable recommendations (vs. just reporting data) Ability to solve ambiguous, unstructured problems Ability to present / explain deliverables to non-technical stakeholders Ability to work within a cross-functional, team-driven structure

Education

A related degree or comparable formal training, certification, or work experience

Physical Demands &

Working Conditions

Thrive in a fast-paced office environment with rapidly shifting market-driven priorities