{"id":2798543,"url":"https://alion.io/job/netflix-machine-learning-scientist-5-forecasting-aggregation","title":"Machine Learning Scientist 5- Forecasting Aggregation","company":{"id":5888,"name":"Netflix","domain":"netflix.com","url":"https://alion.io/company/netflix","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":466000,"max":750000,"currency":"USD","period":"year","gross":null,"usd_annual":750000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Feature Store","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"SQL","optional":false},{"name":"Metaflow","optional":true}],"status":"live","first_seen_at":"2026-08-04T00:00:00Z","employer_posted_date":"2026-08-04","last_verified_at":"2026-10-18T08:00:16Z","board_verified":true,"closed_at":null,"days_open":75,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":75},"description":"At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.\nIn 2022 we launched a new lower-priced, ad-supported tier, and we are building an in-house, world-class ad-tech ecosystem to give our members more choice and to offer advertisers a premium, better-than-linear-TV experience. We are looking for the founding members of this new business area for Netflix.\nThe Ads Forecasting team builds the predictive foundation of the Netflix ads business - the models that tell us, before a campaign ever runs, how much inventory is available and how a campaign will deliver. We forecast supply and demand across audiences, ad products, and formats, and we predict campaign outcomes such as maximum availability, delivery confidence, reach, and frequency, accounting for the ad-serving optimizations that shape delivery. Our forecasts power media planning, underwriting, budget planning, yield, and the public API.\nThis is a brand-new, foundational role. You will build the machine learning models that augment our simulation-based engine for predicting campaign delivery - turning a slow, rules-based simulation into fast, accurate, learnable models of how campaigns deliver against real inventory. You'll own the modeling and prototyping end-to-end and partner closely with our ML engineering team to take models to production.\nIn this role, you will:\nBuild, prototype, and iterate on supervised machine learning modelsthat predict campaign delivery outcomes - delivery risk, reach, frequency, and contention - to replace the current simulation engine.\n\nModel demand-side campaign outcomes while incorporating supply-side signals, so the models reason about how well available inventory matches what advertisers are trying to achieve (targeting, frequency caps, contention, pacing).\n\nDesign rigorous offline and online evaluation frameworksto measure model accuracy, robustness to seasonality and distribution shift, and lift over the simulation baseline.\n\nOwn feature engineeringand contribute to the team's feature store- turning ad-serving logs, campaign attributes, and supply signals into reusable, well-documented features.\n\nPrioritize explainability and interpretability: your models' outputs must be defensible to sales and media-planning stakeholders making real booking and underwriting decisions.\n\nPartner with ML engineers to deploy models at scaleand to monitor production model health and drift, feeding monitoring insights back into the next modeling iteration.\n\nCollaborate with cross-functional partners across product, engineering, and sales to define objectives, constraints, and trade-offs, and to drive adoption of ML-driven forecasts.\n\nCommunicate technical decisions, trade-offs, and results clearly to both technical and non-technical audiences at all levels of the company.\n\nWe are looking for:\nAdvanced degree (PhD or Master's) in Statistics, Mathematics, Computer Science, or a related quantitative field.\n\n5+ years of relevant experience building machine learning models on large-scale data.\n\nDeep expertise in supervised learning(e.g. gradient-boosted trees, regression, and related methods) with a strong bias toward interpretable, explainable models.\n\nStrong feature engineeringskills and familiarity with feature storesand standard ML lifecycle practice (versioning, evaluation, monitoring, retraining).\n\nProven ability to prototype algorithmsand validate them rigorously against production data.\n\nStrong programming skills in Pythonand strong SQL.\n\nWorking knowledge of ad-serving and campaign concepts- how campaigns are delivered and what creates delivery risk: targeting, frequency caps, contention, bidding, pacing, budget planning, and the core campaign objects/attributes; and the metrics that matter (reach, frequency, impressions, clicks, outcomes). You should understand both the supplyside (ad-serving rules and inventory behavior) and the demandside (campaign attributes and advertiser goals). Ads experience is strongly preferred.\n\nAbility to work independently, drive your own projects, and make compelling cases for prioritization.\n\nAbility to communicate technical and statistical concepts clearly to audiences at many levels.\n\nEmbodies the Netflix values while bringing a new perspective to continue improving our culture.\n\nNice to have:\nExperience at a DSP, SSP, or publisher-side ad platform where predicting campaign outcomes at scale is a core science problem.\n\nFamiliarity with our ML stack (Metaflow) or comparable large-scale ML tooling.\n\nExperience partnering with ML engineers to ship and monitor production ML systems.\n\nExperience creating data products, dashboards, or explainability tooling for non-technical stakeholders.\n\nExperience applying GenAI to boost developer/research productivity.\n\nGenerally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $466,000.00 - $750,000.00. This compensation range will vary based on location.Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.\nNetflix is a unique culture and environment. Learn more here.\nInclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.\nWe are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.\nJob is open for no less than 7 days and will be removed when the position is filled.","description_format":"text","description_chars":6992,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Flexible time off","Retirement plans","Stock options"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Streaming & OTT Platforms"],"lifecycle":[{"event":"open","at":"2026-10-18T01:20:47Z"}],"visa":[],"liveness":{"score":17,"band":"cold","label":"Long shot","p_open":1,"p_active":0.464,"p_room":0.36,"age_days":75,"expected_fill_days":38,"reasons":["conf:0","win:tail","crowd:brand"],"computed_at":"2026-10-18T08:40:05Z"},"pay":{"stated_usd_annual":750000,"is_top_pay":true},"html_url":"https://alion.io/job/netflix-machine-learning-scientist-5-forecasting-aggregation","json_url":"https://alion.io/job/netflix-machine-learning-scientist-5-forecasting-aggregation.json","meta":{"generated_at":"2026-10-18T08:40:05Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":12891,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-19T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5888},"rest":"https://alion.io/mcp/rest/get_company?id=5888"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fnetflix-machine-learning-scientist-5-forecasting-aggregation"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fnetflix-machine-learning-scientist-5-forecasting-aggregation"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fnetflix-machine-learning-scientist-5-forecasting-aggregation"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/netflix-machine-learning-scientist-5-forecasting-aggregation\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fnetflix-machine-learning-scientist-5-forecasting-aggregation"}]}