Sr. Engineer, Machine Learning/Artificial Intelligence

Overview STARZ is seeking a technically deep and analytically driven Senior Engineer, AI/ML to find the signals that matter from our data. This role is for someone who thrives on navigating large, complex datasets, applying AI, machine learning, and advanced analytics to surface the patterns, anomalies, and insights that engineering teams need to act on. You will work across STARZ’s Snowflake data warehouse, applying platform intelligence and streaming analytics expertise across video platform playback telemetry, customer care interactions, device & authentication events, and many other domains, as the analytical engineer who converts data into competitive advantage. Responsibilities Proactively explore a wide and growing range of technology data domains including video operational playback and workflows, customer care interactions, device lifecycle, and authentication events to surface hidden signals that go well beyond standard dashboards. Continuously audit the STARZ technology ecosystem for new data sources from network infrastructure and CDN telemetry to workforce and operational systems, evaluating their potential to enrich technology insights and driving their onboarding. Own a repeatable signals framework, defining which KPIs and metrics to monitor, at what thresholds, and why they matter. Apply machine learning and statistical techniques to detect emerging issues, degradation patterns, and risk trends before they appear in operational metrics. Define platform health indicators and alert thresholds ensuring signals are routed to the right teams at the right time with clear escalation paths. Apply ML models including anomaly detection, classification, clustering, and time‑series forecasting as analytical tools to uncover insights. Leverage generative AI and LLMs to accelerate insight generation, automate summarization of logs and telemetry, and augment root‑cause analysis across technology domains. Explore and apply emerging AI capabilities to enhance the speed, depth, and accessibility of insights. Apply AI responsibly by implementing guardrails, grounding, and output validation to ensure insights generated are trustworthy and actionable. Serve as a strategic analytical partner to Engineering, Customer Care, Product/UX, and Executives embedding technology signals into planning, incident response, and prioritization. Establish a signals review cadence with technology leadership and mentor junior analysts to build a broader culture of signal‑driven thinking. Qualifications Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field. 5–8+ years of hands‑on experience in data science, analytics engineering, or a closely related technical discipline. Strong background in SQL and large‑scale cloud data warehouses; Snowflake experience preferred. Hands‑on experience across the ML lifecycle: feature engineering, data quality, anomaly detection, classification, clustering, and time‑series forecasting applied to operational or telemetry data. Familiarity with generative AI, LLMs, and emerging AI techniques (Agents, RAG, Prompt Engineering) in applied analytical contexts. Demonstrated ability to identify signals in noisy operational or telemetry datasets, distinguishing meaningful patterns from statistical noise. Experience in media, streaming, or digital content businesses strongly preferred. Technology & Domain Knowledge Data Platforms & Analytics: Snowflake, transformation frameworks, advanced SQL, BI tooling. ML Frameworks & Libraries: scikit‑learn, TensorFlow, Keras, or equivalent applied to classification, clustering, anomaly detection, and time‑series forecasting on operational and telemetry data. AI & Generative AI: experience with AI Agents, Prompt Engineering, RAG, MCP, AI safety and security practices (guardrails, grounding, output validation). Streaming & Operational Data: Video streaming telemetry (playback events, error taxonomies, CDN logs, QoE/QoS), Kafka / Kinesis or equivalent, pipeline orchestration frameworks, AWS (S3, Lambda, CloudWatch). Statistical Methods: change‑point detection, statistical hypothesis testing, exploratory data analysis. Compensation $150,000 - $180,000 Benefits Full Coverage – Medical, Vision, and Dental. Annual discretionary bonus and merit increase. Work/Life Balance – generous sick days, vacation days, holidays, and wellness days. 401(k) company matching. Tuition Reimbursement (up to graduate degree). About STARZ STARZ (NASDAQ: STRZ) is the leading premium entertainment destination for women and underrepresented audiences, and home to some of the most popular franchises and series on television. STARZ offers a robust programming mix for discerning adult audiences, including boundary‑breaking originals and an expansive lineup of blockbuster movies, and is embodied by its brand positioning “We’re All Adults Here.” Complementary to any platform or service, STARZ is available across a wide range of digital OTT platforms and multichannel video distributors and is a bundling partner of choice. STARZ is powered by an industry‑leading advanced technology, data analytics and digital infrastructure and the highly rated and first‑of‑its‑kind STARZ app. EEO Statement Starz is an equal employment opportunity employer. All employees and applicants are evaluated on the basis of their qualifications, consistent with applicable state and federal laws. In addition, Starz will provide reasonable accommodations for qualified individuals with disabilities. Starz will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable state and federal law. #J-18808-Ljbffr