Senior Data Scientist, Forecasting and Analytics

We are seeking a technically strong and business-oriented Senior Data Scientist to own our forecasting and planning initiatives. This role focuses on turning complex data into actionable insights that improve acquisition, retention, customer experience, and business planning value.

About The Role

• Ownership and maintenance of forecasting models for key marketing and business outcomes, including revenue, demand, conversion, retention, and customer value.
• Support planning cycles by translating historical trends, seasonality, campaign activity, customer behavior, macro factors, and business assumptions into clear forecasts and scenarios.
• Develop planning tools, models, and readouts that help stakeholders and executives understand expected performance, pacing, and business tradeoffs.
• Monitor forecast accuracy, diagnose variance versus plan, and recommend adjustments based on changing business conditions.
• Develop and validate data science models that support customer behavior analysis, segmentation, propensity modeling, churn/retention analysis, personalization, and lifetime value.
• Build analytical frameworks that help stakeholders understand customer needs, behavior drivers, performance trends, and areas for improvement.
• Partner with Marketing, Finance, Product, Analytics, and Data Engineering teams to define business questions, analytical approaches, data requirements, and success metrics.
• Translate complex model outputs into clear recommendations that help stakeholders understand what happened, why it happened, and what actions to take.
• Communicate insights through compelling presentations, dashboards, and executive-ready readouts tailored to executive and other potentially non-technical audiences.
• Proactively identify insights, risks, and opportunities in the data rather than waiting for narrowly defined requests.

Requirements

• 7+ years of experience applying data science, machine learning, forecasting, and/or other advanced analytics in a business environment.
• Strong proficiency in Python, SQL, and common data science or statistical modeling libraries.
• Strong working knowledge of time-series analysis, forecasting, seasonality, trend, lag effects, scenario planning, and model backtesting.
• Experience with marketing analytics, customer analytics, campaign performance, acquisition, retention, conversion, or lifetime value analysis.
• Ability to translate ambiguous business questions into structured analytical plans and actionable recommendations.
• Strong communication and data storytelling skills, with the ability to influence marketing and business stakeholders.

Preferred Qualifications

• Master’s degree or PhD in a quantitative discipline.
• Experience with customer lifecycle analytics, journey analytics, subscription analytics, ecommerce, SaaS, or consumer digital businesses.
• Familiarity with experimentation, causal inference, uplift modeling, survival analysis, Bayesian modeling, or personalization methods.
• Experience working with large-scale customer, behavioral, clickstream, campaign, planning, or transaction-level datasets.
• Experience using cloud data platforms such as Databricks, Snowflake, BigQuery, or similar environments.
• Experience creating executive-ready presentations that connect analytical findings to business strategy and operational decisions.

Benefits

• Bonus Program
• 401k Retirement
• Medical, Dental, Vision, Basic Life, Short Term Disability, and Long-Term Disability Coverage
• Paid Parental Leave
• Support and Community Involvement
• 14 Paid Company Holidays
• Unlimited Paid Time Off for Exempt Employees
• 96 Hours of Sick Time and 120 Hours of Vacation for Non-Exempt Employees Accrued Each Year

Pay

The anticipated compensation for this position is USD $107,430.00/Yr. - USD $176,490.00/Yr. depending on experience and qualifications.

Schedule

This is a hybrid role located within one of our hub locations: Dallas, TX, New York, NY, San Jose, CA, or Newport Beach, CA. You will be required to come into an office on an as-needed basis and work from your home office the rest of the time.