Sr. Specialist, Advanced Analytics

DIRECTV•El Segundo, CA

2d•$100,947 - $183,299•Remote

About The Position

The Sr. Specialist, Advanced Analytics is a seasoned expert in statistical modeling, machine learning, and predictive analytics who operates with a high degree of autonomy to design, build, and deploy sophisticated analytical solutions that drive measurable business outcomes. This role owns the end-to-end lifecycle of advanced models — including customer lifetime value, churn prediction, propensity modeling, and advanced segmentation — and serves as a trusted analytical advisor to business stakeholders and senior leadership. The Sr. Specialist is expected to bring deep technical mastery combined with strong business judgment, translating ambiguous business problems into well-defined analytical frameworks and translating complex model outputs into clear, actionable strategic recommendations. This individual also plays a key role in elevating the capability of the broader analytics team through mentorship, best-practice development, and technical leadership.

Requirements

Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field required; Master's degree or equivalent experience preferred.
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 (e.g., gradient boosting, random forests, clustering, survival analysis) and statistical modeling techniques.
Expert-level proficiency in R and/or Python, with strong SQL skills; experience with cloud-based or enterprise analytical infrastructure strongly preferred.
Proven experience building and deploying customer lifetime value, churn, and segmentation models in a production business environment.
Strong business acumen with the demonstrated ability to translate complex analytical findings into strategic, actionable business recommendations.
Excellent communication and presentation skills, with experience engaging senior business stakeholders and non-technical audiences.
Demonstrated experience mentoring or informally leading junior analysts or team members.

Nice To Haves

Experience with DIRECTV's (or similar) analytical infrastructure and tools is a plus.
May require a background check due to job duties requiring routine access to DIRECTV and DIRECTV customer’s proprietary data. Qualified applicants with arrest and conviction will be considered for employment in accordance with local ordinances and state law.

Responsibilities

Lead the full analytical project lifecycle — from problem framing and data exploration through model development, validation, deployment, and ongoing performance monitoring.
Partner directly with business stakeholders and cross-functional leaders to identify high-value analytical opportunities, scope projects, and align modeling approaches with strategic business objectives.
Translate complex statistical and machine learning outputs into clear, non-technical narratives and actionable recommendations for executive and business audiences.
Proactively identify data quality issues, model drift, or emerging risks in production models and lead remediation efforts.
Independently design, develop, and deploy advanced predictive and machine learning models (e.g., customer lifetime value, churn, propensity-to-buy, next-best-action, advanced segmentation) with minimal oversight.
Establish and maintain model governance practices, including documentation, validation standards, and monitoring frameworks to ensure models remain accurate and reliable in production.
Mentor and coach junior and mid-level analysts on statistical techniques, coding best practices, model validation methods, and business communication skills.
Lead or significantly contribute to large, cross-team analytical initiatives, often coordinating work across multiple contributors and stakeholders.
Stay current on emerging SnowFlake, SQL, Databricks, emerging AI and machine learning techniques, tools, and industry best practices, and champion their adoption where relevant.
Collaborate with data engineering and IT teams to ensure analytical infrastructure, data pipelines, and tooling support scalable model development and deployment.
Present findings, methodologies, and business impact to senior leadership and various cross-functional partners.