Senior Data Scientist

Walmart•Hoboken, NJ

7d•Onsite

About The Position

The Senior Data Scientist develops and implements data science and artificial intelligence solutions to support business decision-making. The role builds, analyzes, and interprets statistical, machine-learning, predictive, and marketing mix models using large-scale datasets to assess business performance, measure marketing investment and campaign effectiveness, identify outcome drivers, and find optimization opportunities. It applies advanced statistical methods such as regression and time-series analysis, develops and validates predictive models, and uses AI and machine learning to support scalable analytics. The position uses Python and SQL to extract, transform, clean, validate, and analyze data, including exploratory analysis and feature engineering. It works with large-scale data environments and cloud platforms, partners with cross-functional teams to translate business requirements into analytical frameworks, and communicates findings and recommendations to stakeholders, including senior leadership. The role also researches and applies emerging data science practices to improve analytical methods and business outcomes. The position is based at 221 River Street, Hoboken, New Jersey.

Requirements

Master’s degree or equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, Engineering, Applied Data Science, or a related field, plus at least 1 year of experience in an analytics-related field; alternatively, a bachelor’s degree or equivalent in one of these fields plus at least 3 years of experience in an analytics-related field.
Experience with statistical modeling and predictive analytics using regression techniques on large-scale business and marketing datasets.
Experience using Python, including Pandas, NumPy, and Scikit-learn, for data processing, modeling, and analysis.
Experience with SQL and relational database querying to extract, transform, and aggregate large datasets.
Experience with exploratory data analysis, data cleaning, and feature engineering for statistical modeling and analysis.
Experience developing, validating, and evaluating machine-learning models using standard statistical and computational techniques.
Experience with time-series analysis and forecasting for evaluating business data trends and patterns.
Experience with causal inference and quasi-experimental methods, including Difference-in-Differences, to measure business and marketing impact.
Experience in marketing and business analytics, including measuring return on investment and channel contribution.
Experience integrating and handling large datasets from multiple sources, including structured and semi-structured data environments.
Experience with data visualization and reporting tools to communicate insights and support decision-making.
Experience designing and evaluating controlled experiments and A/B tests for business applications.
Experience working with cloud-based data platforms for storage, processing, and analysis of large datasets.

Responsibilities

Develop and implement data science and artificial intelligence solutions for business decision-making.
Build, analyze, and interpret statistical, machine-learning, predictive, and marketing mix models using large-scale datasets.
Evaluate business performance and identify key drivers of outcomes, including the impact of marketing and business initiatives.
Measure marketing investment effectiveness and campaign performance, and identify optimization opportunities across channels and campaigns.
Perform regression modeling, time-series analysis, and other advanced statistical and machine-learning analyses.
Develop, train, validate, refine, and evaluate predictive models and algorithms, applying them to new datasets to generate actionable insights.
Ensure model accuracy and robustness through validation, testing, and performance evaluation.
Apply AI and machine-learning techniques to large volumes of structured data and develop scalable analytical solutions.
Write and optimize SQL queries and use Python to extract, transform, and analyze large datasets.
Conduct exploratory data analysis, data validation, data cleaning, and feature engineering to prepare data for modeling.
Use large-scale data environments and cloud-based platforms to process and analyze data.
Collaborate with cross-functional teams to understand business requirements, create analytical frameworks, and deliver insights and recommendations.
Communicate analytical findings to stakeholders, including senior leadership, and translate data insights into business actions.
Research and apply industry best practices and emerging data science, machine-learning, and AI techniques.