Senior Data Scientist: Forecasting & Strategic Analytics

McAfee; New York (NY); Hybrid; USD 107,430 - 176,490; Full time

Benefits offered by this job

Bonus Program

401k Retirement

Medical, Dental, Vision, Basic Life, S

Short Term Disability and Long-Term

Paid Parental Leave

Support and Community Involvement

14 Paid Company Holidays

Unlimited Paid Time Off for Exempt Em

96 Hours of Sick Time and 120 Hours of

Job summary

McAfee is seeking a Senior Data Scientist to own forecasting and planning initiatives across marketing analytics. The role emphasizes forecasting related to revenue, demand, retention, and customer value, translating complex data into actionable insights that influence acquisition and experience.

The ideal candidate excels in time series and ML forecasting, communicates findings to executives, and can frame ambiguous problems, build reliable models, and tell data-driven stories.

Qualifications

• 7+ years applying data science, ML, forecasting in a business environment.
• Proficient in Python, SQL and data science libraries.
• Strong time-series forecasting, seasonality, backtesting.
• Experience in marketing analytics, customer analytics, lifecycle metrics.
• Ability to translate ambiguous business questions into analytical plans and actionable recommendations.

Responsibilities

• Own forecasting and planning models for marketing and business outcomes.
• Translate historical trends into forecasts and scenarios.
• Develop planning tools and readouts for stakeholders and executives to understand expected performance and tradeoffs.
• Monitor forecast accuracy and diagnose variance versus plan, recommending adjustments.

Skills

Strong communication

Data storytelling

Python

SQL

Time-series forecasting

Forecasting

Education

Master’s degree or PhD in quantitative discipline

Tools

Databricks

Snowflake

BigQuery

Job description

McAfee is seeking a Senior Data Scientist to own forecasting and planning initiatives across marketing analytics. The role emphasizes forecasting related to revenue, demand, retention, and customer value, translating complex data into actionable insights that influence acquisition and experience.

The ideal candidate excels in time series and ML forecasting, communicates findings to executives, and can frame ambiguous problems, build reliable models, and tell data-driven stories.