{"id":3368862,"url":"https://alion.io/job/warner-music-group-data-scientist","title":"Data Scientist","company":{"id":1768577,"name":"Warner Music Group","domain":"wmg.com","url":"https://alion.io/company/wmg-com","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":77,"open_postings":12,"ghost_share":0,"stale_share":0.917,"repost_share":0,"time_to_fill_p50_days":38,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["CA"],"hiring_countries_total":1,"salary":{"min":105000,"max":140000,"currency":"CAD","period":"year","gross":null,"usd_annual":99729},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ggplot2","optional":false},{"name":"Hypothesis","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"Python","optional":true},{"name":"R","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-04-13T18:12:32Z","employer_posted_date":"2026-04-13","last_verified_at":"2026-10-30T17:40:34Z","board_verified":true,"closed_at":null,"days_open":200,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":198},"description":"The Role\nMusic is driven by fans, and our data tells their story. We’re looking for a passionate Data Scientist to dive into the massive cultural waves happening across major streaming platforms.\nIn this role you’ll be translating billions of signals into the heartbeat of our listeners. Using advanced statistical methods and machine learning, you’ll uncover hidden listener behaviors, build predictive models for artist and commercial performance, and forecast what fans want next. You’ll also measure and optimize the impact of our global marketing campaigns, using experimentation and data driven insights to understand what truly drives engagement.\nIf you’re excited to turn complex data into cultural insight and real world impact, this is the place for you.\nWhat You'll Do\nAdvanced Forecasting & Modeling: Build robust forecasting models across streaming, audience growth, and commercial performance. Support comprehensive forecasting efforts across deal risk, merchandise, and demand planning.\n\nAudience Insights & Analytics: Gather and analyze audience data to establish best practices for capturing music listening preferences. Detect anomalies and surface actionable insights from large-scale streaming and engagement data.\n\nHypothesis Testing & Storytelling: Transform complex data into compelling stories via tailor-made presentations. Develop new hypothesis testing frameworks and decision-making tools for the real-time assessment of audience viewing patterns and music delivery streams.\n\nCross-Functional Collaboration: Partner with local affiliates, labels, international teams, and Global Technology. You will also collaborate closely with our merchandising teams and business stakeholders to deliver critical reporting and inference.\n\nData Engineering Integration: Interact directly with data engineering teams to identify, define, and secure the data needs required for your mathematical models.\n\nWho You Are\nEducation: Master's degree (or foreign equivalent) in Statistics, Economics, Mathematics, or a highly related quantitative field.\n\nExperience: 5+ years of hands-on DS experience post-BS, or 3+ years of post-Graduate Degree hands-on DS experience\n\nTechnical Stack: 5+ years of hands-on experience working with R, Python, and SQL.\n\nForecasting: At least 5 years of experience building forecasting models across streaming, audience growth, and commercial performance, including evaluating forecast accuracy and continuously improving performance through testing, backtesting, and iteration.\n\nMarketing & Audience Models: At least 5 years of experience using advertising data to build marketing mix models (measuring the incremental impact of ads/campaigns using techniques like multi-linear regression and Hierarchical Bayes).\n\nTargeting & Behavior: At least 5 years of experience building targeting models, including propensity scoring and look-alike modeling, to develop actionable insights into audience behavior.\n\nProduction ML: At least 5 years of experience successfully deploying machine learning models into a production environment.\n\nStakeholder Collaboration: Proven ability to bridge the gap between technical complexity and business logic, serving as a strategic partner who empowers non-technical teams and leadership to make data-driven decisions.\n\nData Visualization: At least 5 years of experience communicating complex technical concepts to non-technical stakeholders using data visualizations (e.g., matplotlib, ggplot2, or similar 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