Data Scientist Jobs

About The Position

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.

Benefits

• Medical, vision, and dental coverage.
• 401(k).
• Stock purchase plan.
• Company-paid life insurance.
• Paid time off, including sick leave.
• Parental leave.
• Family care leave.
• Bereavement leave.
• Jury duty and voting leave.
• Short-term and long-term disability.
• Education assistance, including company-paid college degrees.
• Company discounts.
• Military service pay.
• Adoption expense reimbursement.
• Annual or quarterly performance incentives.