Sr. Specialist, Advanced Analytics

DIRECTV•El Segundo, CA

1d•Remote

About The Position

The Sr. Specialist, Advanced Analytics is a seasoned expert in statistical modeling, machine learning, and predictive analytics who independently designs, builds, and deploys analytical solutions to drive measurable business outcomes. The role owns the full lifecycle of advanced models—including customer lifetime value, churn prediction, propensity modeling, and advanced segmentation—and advises business stakeholders and senior leadership. It translates ambiguous business problems into analytical frameworks and complex model outputs into strategic recommendations. The position also strengthens the broader analytics team through mentorship, best-practice development, and technical leadership. This is a remote position that can be located anywhere in the United States except Alaska. The role may require a background check because its duties include routine access to DIRECTV and DIRECTV customer proprietary data. Qualified applicants with arrest and conviction records will be considered in accordance with local ordinances and state law. The base salary range across DIRECTV’s U.S. labor market zones is $100,947–$183,299. Individual pay depends on work location and factors including job-related skills, experience, and relevant education or training. The stated salary range reflects base salary only and does not include bonus or benefits.

Requirements

Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field.
3–5 years of progressive experience in statistical modeling, predictive analytics, or data science, with a demonstrated track record of independently delivering complex analytical solutions.
Advanced demonstrated expertise in machine-learning algorithms such as gradient boosting, random forests, clustering, and survival analysis, as well as statistical modeling techniques.
Expert-level proficiency in R and/or Python and strong SQL skills.
Proven experience building and deploying customer lifetime value, churn, and segmentation models in a production business environment.
Strong business acumen and ability to translate complex analytical findings into strategic, actionable recommendations.
Excellent communication and presentation skills, including experience engaging senior business stakeholders and non-technical audiences.
Demonstrated experience mentoring or informally leading junior analysts or team members.

Nice To Haves

A master’s degree or equivalent experience.
Experience with cloud-based or enterprise analytical infrastructure.
Experience with DIRECTV’s or similar analytical infrastructure and tools.

Responsibilities

Lead the full analytical project lifecycle, from problem framing and data exploration through model development, validation, deployment, and ongoing performance monitoring.
Partner with business stakeholders and cross-functional leaders to identify high-value analytical opportunities, scope projects, and align modeling approaches with strategic objectives.
Explain statistical and machine-learning outputs in clear, non-technical narratives and actionable recommendations for executive and business audiences.
Identify data quality issues, model drift, and emerging risks in production models, and lead remediation efforts.
Independently design, develop, and deploy predictive and machine-learning models, including customer lifetime value, churn, propensity-to-buy, next-best-action, and advanced segmentation models.
Establish and maintain model governance practices, including documentation, validation standards, and monitoring frameworks.
Mentor and coach junior and mid-level analysts in statistical techniques, coding practices, model validation, and business communication.
Lead or substantially contribute to large cross-team analytical initiatives, coordinating contributors and stakeholders as needed.
Stay current with Snowflake, SQL, Databricks, AI and machine-learning techniques, tools, and industry practices, and champion relevant adoption.
Collaborate with data engineering and IT teams to support scalable analytical infrastructure, data pipelines, and model deployment tooling.
Present findings, methodologies, and business impact to senior leadership and cross-functional partners.